Elfried Samba is the Co-Founder and CEO of Butterfly Effect - the agency brands call when they want to build real community and cultural relevance.

Before launching Butterfly Effect, Elfried was Global Head of Social Content at Gymshark, where he helped scale its fitness community from 1.5 million to over 20 million followers, playing a key role in the brand’s journey to a £1 billion valuation.

In its breakout year, Butterfly Effect partnered with Netflix, Bumble, McDonald’s, WeTransfer, Meta and Square - cementing its position as a leading force in community-first marketing.

Elfried’s personal LinkedIn presence has redefined what influence looks like in B2B. 
With 500M+ impressions, 9M+ engagements, and 360K+ followers, he now ranks as the second most engaged LinkedIn creator globally - proving that personal brand isn't just a nice-to-have, it's a growth engine.

Elfried Samba

The scariest place to be
is the same place as last year.


Not because life didn’t happen - but because nothing changed on purpose.


End-of-year reflection isn’t about guilt.
It’s about honesty.


Ask yourself:

- What did I tolerate this year that held me back?

- Where did I choose comfort over progress?


- Which habits, people, or patterns stayed the same because changing them felt uncomfortable?


Comfort is the most expensive thing on earth.
It costs you momentum, edge, and time you never get back.


Growth doesn’t require chaos.
But it does require intentional discomfort.


Pressure is good.
Resistance is good.
That’s how capacity expands.


What’s dangerous is friction that teaches you nothing - the same conversations, the same excuses, the same energy drains.


As the year closes, don’t just reflect.
Decide.


Decide what doesn’t come with you.
Decide where you raise the standard.
Decide how next year looks different - on purpose.


That’s how you make sure you’re not standing in the same place again.

CEO & Co-founder @ Butterfly Effect | Ex-Gymshark Head of Social (Global)

Until it’s my turn,
I’ll keep clapping for others.

Not because I’m naïve.
Because I’m secure.

Abundance isn’t having more.
It’s needing less.

It’s understanding that someone else winning doesn’t delay you.
It proves it’s possible.

And the truth is…
Your reaction to other people’s success is a mirror.

If it makes you bitter, you’re operating from scarcity.
If it makes you inspired, you’re operating from vision.

I’ve learned this the hard way:

The people who stay consistent…
stay humble…
stay generous…
are the ones who end up unstoppable.

So celebrate loudly.
Support publicly.
Clap even when nobody claps for you.

Because your time will come.

And when it does,
you’ll want your hands clean.

Most people don’t realise…


Life doesn’t always reward you instantly.
It rewards you eventually.


And that gap between “eventually” and “now” is where most people lose themselves.


They start rushing.
They start comparing.
They start questioning God, questioning timing, questioning their worth.
They start moving like the dice has already stopped rolling - when it hasn’t.


But here’s the truth:


Sometimes the dice is rolling for years.
Not because you’re unlucky.
But because you’re being shaped for what you asked for.


You said you wanted more.
More influence.
More peace.
More wealth.
More impact.


And those things don’t arrive in small packaging.


So life stretches you.
Tests your character.
Checks your ego.
Puts you in rooms where you feel behind.
Makes you watch other people win first.


Not to punish you.
But to teach you what winning actually costs.


Because real success isn’t just getting what you want.


It’s becoming the type of person
who can hold it
without destroying themselves.


And while the dice is still rolling…


You clap for others.
You show love.
You stay generous.
You stay humble.
You keep your heart clean.


Because bitterness delays blessings.


A jealous spirit can’t carry abundance.
A resentful mindset can’t sustain wealth.
And a small heart can’t run a big life.


So if you’re in the waiting season, don’t panic.

No alternative text description for this image

When the phone was tied to a wire… people were free.


Not because life was easier.
Because attention wasn’t stolen.


Back then, your time had borders.


Now your phone follows you into:

- Meetings
- Dinner
- Sleep
- Conversations
- Silence


And we call it “normal.”


But the truth is:

If you don’t control your attention, you don’t control your life.


You don’t need more discipline.


You need more distance.


Turn off what doesn’t pay you.
Mute what doesn’t build you.
Protect what matters like it’s your last resource.


Because it is.


Your attention is the new land.
And most people are letting it get colonised.

The answer is within you.
But there’s a lot of noise around you.


Noise from timelines, opinions, pressure, and other people’s definitions of success.
It’s not that you don’t know…
It’s that you can’t hear yourself.


Clarity doesn’t come from more information.
It comes from less interference.


So if you feel lost, don’t chase a path.
Pursue yourself.
Get healthier. Get calmer. Get stronger. Get honest.


Because when you become aligned…
you stop looking for direction.


You become it.

Credit: Tracie Lipscomb Linderman MBA, CCMP

A lot of people aren’t ready for who you’re about to be.

Because they’re judging your future
based on your present conditions.

They’re looking at the draft…
and acting like it’s the final version.

But the truth is simple:

Time transforms.

It turns coal into diamond.

And right now?
You might still look like coal to people who don’t understand pressure.

They see the late nights.
The quiet seasons.
The rebuilding.
The “still figuring it out” phase.

And they assume that’s the end of the story.

But that’s what small minds do:

They judge the chapter…
instead of reading the whole book.

Most people don’t know what to do with someone who’s evolving.

Because when you change, it forces them to confront what they haven’t.

Your discipline makes their excuses louder.
Your consistency makes their “one day” look weak.
Your standards make their comfort zone feel embarrassing.

So they start acting funny.

They call you “different” like it’s a warning.
They say “you’ve changed” like it’s a problem.
They bring up the old version of you like it’s the real one.

But here’s the part they don’t want to accept:

You are supposed to change.

You are supposed to upgrade.
You are supposed to outgrow the room.

Staying the same so people can feel comfortable around you
is one of the most expensive decisions you can make.

And it always starts subtly.

You start editing yourself.
Softening your ambition.
Making your goals sound smaller.
Shrinking your confidence.
Holding back so nobody feels left behind.

That’s not humility.

That’s fear.

And fear will keep you trapped in “present conditions” forever.

Let me say it clean:

If someone can only respect you when you’re struggling…
they don’t deserve access to you when you’re shining.

Because diamonds don’t get made in comfort.

They get made in pressure.

So if you’re in a hard season right now…

Good.

That means the transformation is working.

And if certain people can’t handle the next version of you…

Let them go.

Not everyone is built to witness someone become extraordinary
without taking it personally.

Don’t argue with them.
Don’t explain yourself.
Don’t beg to be understood.

Just keep building.

Because the same people judging you while you’re coal…
will be the first ones acting like they always saw the diamond.


Credit: Dr. Elizabeth Lindsey

Who you’re around matters more than what’s in your calendar.


Pressure is good.
Resistance is good.
Challenge makes you stronger.


But bad energy isn’t resistance - it’s friction with no upside.


In 2026, we’re choosing environments that actually make us better.


That means:
	•	No drama. No negativity.
	•	Higher goals. Higher motivation.
	•	People who challenge you to grow, not drain you to cope.


Good energy doesn’t mean easy.
It means aligned.
It means honest feedback without ego.
Push without jealousy.
Standards without resentment.


We’re surrounding ourselves with people who want to win together,
who raise the bar,
and bring out the best in each other.


That’s the energy we’re on next year


Credit: Dora Vanourek

graphical user interface, text, application, chat or text message

Stop comparing yourself to people who started earlier.


You’re not late.
You’re just early in your chapter.


Everyone’s journey is different -
because everyone’s timeline is different.


And most of what you’re comparing yourself to…isn’t even real.


You’re seeing someone else’s highlight reel
and comparing it to your behind-the-scenes.


You’re measuring your present
against their past.


But time doesn’t work like that.


Progress isn’t linear.
Growth isn’t visible.
And the work that changes your life
usually happens quietly first.


Some people get a ten-year head start.


Some people lose five years to survival.
Some people restart at thirty.
Some people arrive at forty.
Some people peak twice.


That doesn’t make you behind.


It just means you’re building on a different schedule.


And if you stay locked in long enough…
your “late start” becomes your advantage.


Because when you’ve had to fight for every inch, you don’t waste momentum.


Comparison is a distortion.
Focus is the truth.


Keep going.


Credit: Marshall Goldsmith

Most people confuse status with security.

The title.
The promotion.

The seat in the meeting that suddenly makes people listen.

It feels solid  until you realise how fast it can stop meaning anything.

For years, I watched people build their entire identity around what the company gave them.

And I watched others quietly build something no one could touch.

The difference shows up when pressure hits.
One group has the title.

The other has leverage.

Because real security isn’t external.

It’s internal.

Your skills.
Your judgment.
Your reputation when you’re not in the room.
Your ability to solve problems without being told what to do.

That’s why some people always seem “in demand.”

Not because they’re loud but because they’re useful.

They’ve spent years:

 - Learning in public
 - Helping without keeping score
 - Building real relationships (not networking theatre)
 - Getting so good at their craft that people come to them


Meanwhile, others struggle the moment the title stops doing the talking.

Because when the role fades, there’s nothing underneath it.

Some people rent their identity from their employer.

Others own it.

You can’t control the market.
You can’t control leadership decisions.
You can control what you build inside yourself.

That’s long term thinking.

And it compounds.

I write about this every Saturday.

One focused email.

4 minutes to read.

For people who want careers and businesses that don’t rely on permission.

Credit Ryan Yockey

Ama Amo-Agyei  is one of the most inspiring founders I know. 

Her journey. Her story, Her words. 

The part of the entrepreneurial journey most don’t speak about. 

Let her cook.

New Bottleneck Podcast episode out now.

Links in the comments 🙏🏽

RBC Capital Markets

- Lead the requirements gathering and creation of data quality rules for a new regulatory initiative (T+1).
- Manage 2 contract workers in drafting requirements, reviewing data lineage and creating be-spoke data quality rules across multiple quality 
 dimensions.
- Built a Tableau report all data quality controls created with meta-data on schema and other useful data elements to help reduce the time in the drafting of new data controls by 20%
- Built a Python script to Extract Deeply Nested JSON from S3 and Tranform into a CSV format for easy processing and analysis. (Avg size of data set is 600MB across 10 different instances with over 10 different critical data elements)

Senior Data Analyst

• Resolved a data quality issue of missing maturity dates on outstanding loans with a material impact of 122 billion USD.
• Collaborated with finance and regulatory reporting stakeholders to implement business requirements, resulting in a 25% increase in data quality and adherence to compliance guidelines as part of an enhanced enterprise data governance framework.
• Utilized Tableau to create an intuitive schema and data quality report, leading to a 35% enhancement in departmental rule design efficiency at RBC Capital Markets.
• Engineered data pipeline processes using Airflow and Python, reducing data processing times by 30% for faster insights delivery and improved efficiency in strategic decision-making.
• Collaborated with Regulatory Reporting and Finance IT stakeholders to enhance data quality and efficiency using DBT, resulting in a 25% reduction in data-related errors.
• Developed SQL data models on a cloud data warehouse and administered a Dremio data lake, handling over 10TB of data.

Data Analyst

Data Achievers

•  Worked with a variety of clients to support business intelligence reporting and data quality assurance.
•  Monitored product performance and customer satisfaction metrics to identify areas of improvement, resulting in a 40% increase in user engagement and a 25% increase in revenue.  
• Developed and maintained product roadmaps and feature backlogs, resulting in a 30% increase in team efficiency and a 20% increase in project success rate. 
• Created and maintained product dashboards and reports for job seekers, resulting in a 25% reduction in decision-making time and a 15% increase in data accuracy.

Data Scientist

TALENT™ (Ontario Tech Talent)

• Supported a self-paced, self-directed skills development experience (“Self Paced Experience”) for students looking to upskill in data analytics;
• Asynchronously engaged with learners (e.g. discussion forum moderation) via email, chat, or LMS like Canvas
• Provided 1:1 learner support every week; offered as virtual appointments booked through Calendly
• Effectively and efficiently verified customer skill acquisition by way of reviewing all artifacts and other work products presented for verification by learners against approved standards
• Recorded skills verification decisions in TALENT’s system of records

Skill Coach

• Demonstrated exceptional communication skills and exercised diligence in identifying delinquent loans, credit cards, credit lines, or mortgages, and referred clients to Collections.
• Established and maintained relationships with individual or business customers by offering value through every interaction.
• Identify fraudulent transactions at the first point of contact and work with AML and Fraud teams to investigate the transaction to ensure minimal client impact.
• Actioned solutions relevant to client banking needs, with a 30% action rate weekly, to support team goals.

Client Data Analyst

IKEA

- Enhanced order fulfillment Tableau dashboards, improving operational visibility and increasing efficiency by 15%. 
- Analyzed operational data to optimize processes, leading to a 25% improvement in service levels. 
- Collaborated with teams to implement data-driven strategies, reducing inventory errors by 20%

Operations Analyst Intern

Walmart Canada

•	Provided exceptional customer service to clients on the sales floor, ensuring that all their questions are answered promptly and efficiently.
•	Acted as the first point of contact to customers' challenges with finding merchandise on the sales floor.
•	Contributed to team success by supporting colleagues with their workload to effectively deliver on customer promises.
•	Built and maintained a strong relationship with colleagues to improve the quality of work and increase customer satisfaction.
•	Managed safety risk on the sales floor by proactively identifying and removing potential safety issues.

Fulfillment Associate

Fond Regional Pour La Promotion de la Sante du Centre (FRPSC)

• Implemented an analytical framework that reduced transportation costs by 20% by determining the best delivery route for urgent medicine delivery across geographical regions.
• Built dashboards using Tableau to help track Key Performance Indicators (KPIs) thereby increasing visibility on key business metrics.
• Conceptualized a new service delivery design which when implemented improved service levels by 25%.
• Analyzed 6 business processes to collect measurable data.
• Improved the existing demand forecasting software performance by modeling data sources to be compatible with schema.
• Maintained inventory ERP system by ensuring consistency between inventory on-hand and inventory in the system

Logistics Analyst

Cam-Green

• Planned and developed content strategies.
• Built strong relationships with project team members and stakeholders to ensure the achievement
of set goals.
• Assessed business opportunities and develop strategies to attract venture capital.
• Responsible for drafting a business plan for validating the business model.
• Provided consultation on planning and starting new businesses.
• Prepared business proposals and reports for presentation to stakeholders.
• Conceived and implemented a sales development program for the launch of the project which led
to a 110% achievement of the sales target.
• Developed media campaigns across social media outlets and campus bulletins which galvanized
support from key government officials.
• Worked closely with the project manager to ensure that sales and marketing efforts are consistent
with project goals.
• Complaint with established guidelines set by the University Board sponsoring entrepreneurial
initiatives.

Social Media Manager

I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I love to identify hidden business opportunities that lie in data and bring them to life with compelling visualizations.  I spend time setting clear goals, plans of action, and risk mitigation strategies for data analytics projects before even collecting data. 

I am a data analytics professional with a strong background in supply chain and marketing. I am passionate about data engineering. I love a good journey🚘, be it a journey in an RV truck across country, a learning journey📝 , or a customer journey with multiple touch points, or a data pipeline from source system to end user application. I am always curious to know what I can learn about a customer's journey to ensure it is seamless.😊

At RBC I have developed my customer-focused problem-solving mindset at the awareness stage and consideration stages of the business's marketing funnel. I worked closely with partners across the delivery channels (branch and telephone) to deliver exceptional customer service to clients to meet their banking needs. 

At RBC Capital Market, as a Senior Data Analyst, I own the process of ensuring data sent to report owners are fit for purpose (without data quality issues). Build data quality rules using SQL. Build Tableau dashboards to monitor data quality of critical data elements (CDE's) in production 

My experience working with cross-functional teams and creating a sense of synergy in the achievement of a common goal has proven to be a valuable value add. I helped in the resolution of data quality issues by proposing changes to how data is transformed both upstream and downstream.

I am always on the lookout for technologies that improve my workflow; I will take a week or so and dive deep into a data technology to see if it’s a good fit for a side project I'm working on and put aside if not. 

When I'm not working I am with my family making sure we are living every moment and making it count. You can also find me with my headsets listening to the next big rising artist of our generation and exploring new genres of music - I am a big lover of Afrobeats and Cameroonian cuisine 🇨🇲 

---
Current Status: Helping folks get their First Data Analyst roles - DM me to learn more

Peter Ndiforchu

If I were learning Data Analytics all over again in 2026, I’d do this very differently.

Not because the tools changed.
But because the market did.

Here’s what I’d prioritize from Day 1:

1️⃣ I’d stop “collecting skills” and start solving business problems
SQL, Python, Power BI… none of these matter in isolation.
I’d anchor every learning sprint to a real business question: revenue, cost, risk, growth.

2️⃣ I’d build fewer projects—but make them decision-grade
No more dashboards that just “look good.”
Every project would answer:
• What decision is this enabling?
• Who is the stakeholder?
• What action follows this insight?

3️⃣ I’d treat communication as a core technical skill
Charts don’t drive impact. Narratives do.
I’d learn how to explain insights to non-technical leaders before learning another library.

4️⃣ I’d learn AI as a force multiplier, not a shortcut
AI to accelerate analysis.
AI to explore scenarios.
AI to think deeper.
Not AI to replace understanding.

5️⃣ I’d optimize for leverage, not job titles
Instead of asking “How do I become a Data Analyst?”
I’d ask: “Where can data create asymmetric value in this business?”

Because in 2026,
Data professionals aren’t hired for dashboards.
They’re trusted for judgment.

If you were starting over today, what would you do differently?

---
👉 RSVP now for Saturday, Jan 10th
If you’re planning for a data role in 2026, this session will help you focus on what actually moves the needle:
https://luma.com/2qh65716

#52DaysDataChallenge

I help mid/senior level, non-technical professionals get their first data analyst role | Senior Data Analyst @Autodesk |Top rated Data Career Coach on topmate.io

If your 2026 data analytics learning plan is still “SQL → Dashboard → Apply,” you’re already behind.

Five months ago, a post I shared struck a nerve:

“Entry-level data analysts who only know SQL will be unemployable in 2 years.”

That reality hasn’t softened.
It’s intensified.

In 2026, SQL is table stakes, not a differentiator.

What’s changing isn’t what you learn first — it’s what you signal while learning.

The 2026 Data Analytics Learning Plan (That Actually Converts)

1️⃣ Learn SQL — but don’t stop at syntax
Everyone can write a query.
Few can explain why the metric matters, who it’s for, and what decision it unlocks.

➡️ Practice SQL as a decision-support tool, not a technical flex.

2️⃣ Add one analytical “lens,” not five tools
Instead of:

→ SQL

→ Python

→ Tableau

→ Power BI

Pick one business lens:

→ GTM

→ Growth

→ Finance

→ Product

→ Operations


Then learn tools through that lens.

3️⃣ Build fewer projects — but make them uncomfortably real
In 2026, recruiters scan portfolios asking:

“Could this person survive ambiguity?”

“Can they explain tradeoffs?”

“Do they think beyond the dataset?”


➡️ One strong, messy, decision-driven project beats five polished Kaggle clones.

4️⃣ Learn to narrate before you automate
AI can now:

→ Write queries

→ Generate charts

→ Summarize tables


What it still can’t do well:

→ Read the room

→ Push back on bad questions

→ Translate insight into action


Your learning plan must include storytelling, stakeholder framing, and judgment.

5️⃣ Optimize for roles, not courses
The fastest learners in 2026 aren’t asking:

“What should I learn next?”

They’re asking:

“What does a hiring manager need this role to solve in 90 days?”

Then they reverse-engineer from there.


2026 won’t reward the most certified analyst.
It will reward the one who learned with intent.

SQL opens the door.
Signal keeps you in the room.

If you were rebuilding your data learning plan for 2026, what would you remove first?
---
👉 RSVP now for Saturday, Jan 10th
If you’re planning for a data role in 2026, this session will help you focus on what actually moves the needle:
https://luma.com/2qh65716

#52DaysDataChallenge

Helping Non-Technical Professionals Build Data-Driven Careers That Travel Globally | Data Career Strategist | Senior Data Analyst @ Autodesk

One mistake quietly delayed my growth in data analytics.

For 6 months, I did everything right on paper.

I studied Python.
Dabbled in cloud computing.
Chased every “must-have” skill I thought a serious data analyst needed.

My plan?
Be fully ready before putting myself out there.

The result?
I stayed invisible.

No projects shipped.
No clear story.
No signal to the market that I could solve real problems.

Perfection became a hiding place.

What I learned later is simple—but uncomfortable:

Careers don’t reward completeness.
They reward clarity.

The biggest jumps in my career didn’t come from learning more tools.
They came from choosing one problem, one stack, and one outcome—and shipping it publicly.

If you’re early in your data career, this might save you months:

→ Depth beats breadth early
→ Finished projects beat endless prep
→ Imperfect action beats silent mastery

If this sounds like you, don’t wait another 6 months.

Pick one dataset, solve one real problem, and share one insight this week.
Not when it’s perfect. When it’s done.

Post the chart.
Explain your thinking.
Let the work speak.

Momentum comes from visibility—not readiness.

Most people think careers are built on skills.

They’re wrong.

Careers are built on signals.

Here’s a simple framework I use when coaching candidates
Skills vs Career Signals
(With real examples from Anika and Samuel 👇)


1️⃣ Skills = What you can do

These are capabilities you’ve learned.

Examples
• SQL
• Python
• Power BI
• Stakeholder communication
• Experiment design

Skills are necessary.
But they’re invisible until activated.


2️⃣ Career Signals = Proof the market can see

Signals answer one question for hiring managers:

“Can I trust this person to deliver in my environment?”

Examples
• Projects tied to real business outcomes
• Dashboards used by actual stakeholders
• Clear problem framing, not just analysis
• Consistent storytelling in interviews and online

Signals reduce hiring risk.
That’s what gets interviews.


3️⃣ Anika’s shift

Anika had strong technical skills.
Courses. Certificates. Practice notebooks.

What changed her trajectory wasn’t learning more tools.
It was turning skills into signals.

She reframed projects around business questions.
Documented tradeoffs.
Shared her thinking publicly.

Same skills.
Stronger signals.
Different results.


4️⃣ Samuel’s shift

Samuel focused early on visibility.

Instead of saying
“I know SQL”

He showed
• How SQL improved decision speed
• Why his metrics mattered to leadership
• How he handled messy, ambiguous data

He didn’t out-skill others.
He out-signaled them.


5️⃣ The strategic takeaway

If you’re stuck, ask yourself:

Am I learning skills
or am I broadcasting signals?

The fastest career growth happens when you do both
but prioritize what the market rewards.

Skills get you ready.
Signals get you chosen.


If you’re building your data career in 2026, optimize for signals.
That’s how you move from capable to credible.

I went from stocking shelves at Walmart… to Times Square.
Not by learning more skills — but by sending clearer signals.

The path wasn’t straight.
That’s the lesson.

2025 was the year I stopped collecting skills and started packaging proof.

Here’s what actually happened 👇

→ Left RBC Capital Markets after years building data products in regulated environments
→ Joined Autodesk as Senior Data Analyst (July)
→ Featured on a Times Square billboard as a Top Career Coach
→ Ranked as #1 Data Science Creator in Cameroon according to Favikon 
→ Named one of Canada’s Top 100 Mentors on topmate.io
→ Launched the 52-Day Data Challenge
→ Helped 100+ people land data roles through coaching

But the milestones aren’t the takeaway.

The real value came from what I had to unlearn.

5 truths 2025 made unavoidable:

1. Careers are built on signals, not skills.
Skills are invisible until activated. Signals are proof the market can see.
Your portfolio isn’t a gallery — it’s a sales page for your judgment.

2. AI is a force multiplier, not a replacement.
The best analysts aren’t fighting AI. They’re partnering with it.
Stop competing on tools. Start competing on thinking.

3. “Unrelated” experience is the backdoor.
I wasn’t hired despite retail — I was hired because of it.
Context makes data valuable.

4. The hardest data skill isn’t technical.
It’s learning to ask the right questions before opening any tool.
Great analysts clarify problems, not just answer them.

5. Outcomes beat outputs. Every time.
No one remembers the spreadsheet.
They remember the decision your work unlocked.

Top posts this year:

→ “Entry-Level Data Analysts who only know SQL will be unemployable in 2 years”
https://lnkd.in/eyPk-VrF

→ “The hardest skill I had to learn in data had nothing to do with data”
https://lnkd.in/eyrpBwwq

→ “The moment I realized I wasn’t just a report generator”
https://lnkd.in/ewS94YZi

→ “The ‘backdoor’ into data is wide open”
https://lnkd.in/ePuCxKwH

To everyone who engaged, shared, or took action — you shaped this more than you know.

Special shoutout to Kelachi Charles-Beke and every client who trusted the process.
Your wins are the receipts.

And to my wife Barbara Mbingno — the real rocket fuel behind everything I build.


I had yesterday’s struggles.
2026 holds tomorrow’s opportunities.

What’s next: → Deeper coaching for mid/senior professionals
→ More decision-grade frameworks
→ Less noise. More signal.

If you’re building your data career in 2026:

↳ Comment “HIRED” for cohorts + 1:1 coaching
↳ Comment “JANUARY” for early access
↳ Comment “WORKSHOP” if you want me to speak to your team

Tools get you shortlisted.
Impact gets you hired.

What’s one career move you’re committing to in 2026?

I’ve interviewed very strong data candidates who never made it past round 1

Not because they lacked skills.
Not because they couldn’t code.
Not because they didn’t “know enough.”

They failed the first round for quieter, more strategic reasons most people underestimate.

Here’s what usually went wrong 👇 

1. They answered questions, but never framed a problem
They jumped straight into tools and techniques.
Great analysts start by clarifying why the problem exists and what decision is at stake.

Round 1 is not a skills test.
It’s a thinking test.

2. They treated SQL, Python, or dashboards as the outcome
Hiring managers don’t hire queries.
They hire people who reduce risk, save time, or make money.

If you can’t clearly connect your work to a business outcome, your technical depth won’t save you.

3. They talked like executors, not partners
Phrases like: “I was asked to…” “I just built what they wanted…”

Signal someone waiting for instructions.

Strong candidates speak in terms of trade-offs, constraints, and recommendations.

4. They couldn’t explain impact without jargon
If your explanation only makes sense to another analyst, you’re limiting your ceiling.

Round 1 often includes non-technical stakeholders.
Clarity beats complexity every time.

5. They underestimated storytelling
Same project.
Same dataset.
Different outcomes.

The candidates who moved forward could tell a clean story: 
problem → approach → insight → decision → result.

Here’s the uncomfortable truth:

Most round-one rejections are not about technical gaps.
They’re about signal.

The interview is asking: “Can I trust you to think with the business, not just for it?”

If you’re preparing for data interviews, optimize for that signal.

Skills get you the interview.
Thinking gets you past round 1

Why Strong Data Candidates Fail Round 1

Most candidates think they’re being evaluated on what they built.

They’re not.

When you explain a project, hiring managers are quietly assessing something much deeper.

Here’s what’s actually happening behind the scenes 👇

1️⃣ Can you think in business outcomes—or just tasks?
If your story starts with “I used SQL to…” and ends there, that’s a red flag.
They’re listening for why the project existed and what changed because of it.

2️⃣ Do you understand the problem—or just the solution?
Strong candidates explain the constraint, the tradeoff, and the decision.
Weak ones jump straight to the dashboard.

3️⃣ Can you simplify complexity?
If you can’t explain your project to a non-technical stakeholder in 60 seconds,
they assume you’ll struggle in real meetings.

4️⃣ Did you drive the work—or were you just assigned it?
Ownership shows up in language:

“I noticed…”

“I proposed…”

“I pushed back when…”


That’s leadership without the title.

5️⃣ Do you sound like someone they can put in front of executives?
Clarity. Structure. Calm confidence.
That’s what earns trust.

Here’s the uncomfortable truth:

Two candidates can build the same project.
Only one gets the offer—because they frame it differently.

Your projects don’t get you hired.
Your interpretation of them does.

If this resonated, next time you explain a project, ask yourself:
“Am I telling a story—or reciting a task list?”

What Data Hiring Managers Really Evaluate on Interviews

These two schedules look similar.
One builds a data career.
The other keeps you stuck.

At first glance, the difference feels small.
An hour here.
Thirty minutes there.

But zoom out.

One version of the day is reactive.
The other is intentional.

One leaks energy through scrolling, snoozing, and noise.
The other compounds skills, relationships, and confidence.

Here’s the uncomfortable truth 👇
Most people don’t lose in their careers because they lack ability.
They lose because their days are designed by default.

Learning SQL consistently won’t feel exciting.
Networking on LinkedIn won’t feel urgent.
Reading one chapter won’t feel productive.

Until it does.

Because habits don’t change your life overnight.
They change your trajectory.

So the real question isn’t which schedule looks better.
It’s which one looks more like your future self.

A or B?

Drop it in the comments.

Day 34 of #52DaysDataChallenge

Most candidates know the STAR method.
Very few use it in a way that actually signals seniority in data roles.

Here’s how hiring managers experience it 👇

The STAR method for data roles (with a real example)

S — Situation
❌ Weak: “We didn’t have good dashboards.”
✅ Strong: “Sales leaders were making weekly decisions using spreadsheets pulled manually from three systems.”

Why this matters:
You’re showing business context, not just a messy data problem.

T — Task
❌ Weak: “I was asked to build a dashboard.”
✅ Strong: “I was responsible for creating a single source of truth that leadership could trust for forecasting.”

Why this matters:
This reframes you from executor to owner.

A — Action
❌ Weak: “I used SQL and Tableau.”
✅ Strong:
“I audited existing tables, wrote validation queries to reconcile numbers across sources, aligned definitions with Sales Ops, and designed a dashboard focused on decision-making—not vanity metrics.”

Why this matters:
Tools are expected. Judgment is not.

R — Result
❌ Weak: “They liked the dashboard.”
✅ Strong:
“Forecast variance dropped by 18%, manual reporting time was reduced by 6 hours per week, and the dashboard became the default reference in leadership meetings.”

Why this matters:
Impact beats effort. Every time.

The real mistake candidates make:
They treat STAR like a storytelling template.
Hiring managers hear it as a decision-making framework.

In data roles, STAR isn’t about what you built.
It’s about:

How you understood the business

How you made trade-offs

How your work changed behavior


If your STAR answers don’t reveal those signals,
you’re underselling yourself—no matter how strong your skills are.

📌 Save this before your next interview.

How I went from Cameroon → Canada → a US data role (without ‘local experience’).

When I moved, I assumed the biggest hurdle would be visas, credentials, or “local experience.”

It wasn’t.

The real question global recruiters were silently asking was:
“Who can vouch for this person when we can’t observe them directly?”

Here’s what made the difference on my journey 👇

1/ Relationships reduce hiring risk
Recruiters outside your country can’t see your work day-to-day. They rely on people they trust to validate your judgment and impact.
I focused on building genuine connections with:

Hiring managers

Senior data practitioners

People in the ecosystem who could confidently say,

 “I’ve worked with him. He delivers. I’d hire him again.”

2/ Visibility beats invisibility
I didn’t just rely on a resume. I made my work and thinking visible by:

Sharing real projects publicly

Discussing trade-offs and decisions

Engaging with communities in LinkedIn, Slack, and Discord

This gave recruiters context before even meeting me.

3/ Skills matter, but signals win
SQL, Python, Excel—all necessary. But what got me hired was showing:

Structured thinking

Clear communication of impact

Ability to navigate ambiguity

Coupled with endorsements from people who could vouch for me, geography became irrelevant.

Global hiring isn’t about ‘local experience’—it’s about trusted signals.

Ask yourself: “Who in this market can confidently vouch for my work?”

That’s the real game.

Cameroon → Canada → a US data role

The first time I realized hiring expectations weren’t universal…
I thought I had done everything right.

Strong technical skills.
Solid projects.
Clear results.

Then I walked into an interview outside my home market and something felt off.

They weren’t impressed by what tools I knew.
They kept probing how I worked.

Questions like:

“How did you influence stakeholders who didn’t agree with you?”

“What trade-offs did you make under pressure?”

“Who trusted your judgment and why?”


That’s when it clicked.

In some markets, hiring is about capability.
In others, it’s about credibility.
And in global roles, it’s about signal alignment.

Same resume.
Same experience.
Very different evaluation lens.

I stopped optimizing for: 
❌ tool checklists
❌ keyword stuffing
❌ “local experience” myths

And started optimizing for: 
✔ decision-making stories
✔ business judgment
✔ proof that someone would trust me in the room

That shift changed everything.

Global hiring isn’t harder.
It’s just calibrated differently.

If you’re preparing for roles beyond your home country, ask yourself: Are you proving skills…
or earning confidence?

Because globally, confidence hires before competence.

2025 reminded me why this work matters.

Looking back, I’m grateful for every conversation, every connection, and every moment of growth on topmate.io 

This Year in Review shows the numbers, but the real impact goes far deeper than stats. It’s in the clarity, confidence, and momentum built together.

Thank you to everyone who trusted the process.

Onward.

#Topmate2025Recap #52DaysDataChallenge

Here’s the uncomfortable truth most candidates don’t hear early enough:

Most data portfolios aren’t evaluated as “work.”
They’re evaluated as signals.

And most of them send the wrong ones.

I’ve reviewed a good number of portfolios as part of the hiring team.
The pattern is consistent:

What candidates show:
• Dashboards with no business question
• Kaggle-style analyses disconnected from real decisions
• Tools-heavy walkthroughs (“I used SQL, Python, Tableau…”)
• Perfect charts that solve imaginary problems

What hiring managers are actually scanning for:
• Can this person frame a messy business problem?
• Do they understand tradeoffs, not just techniques?
• Can they explain why something mattered — not just how they built it?
• Would I trust them to influence a decision with imperfect data?

Here’s the part most people miss:

A portfolio isn’t proof of skill.
It’s evidence of judgment.


That’s why two candidates can use the same dataset —
and only one gets a callback.

The one who: 
• Anchors the project to a real stakeholder decision
• Explains assumptions and constraints
• Shows what they didn’t do and why
• Communicates impact in plain business language

Strategic shift to make today:

Stop asking, “How do I show more tools?”
Start asking, “What decision does this help someone make?”

That question alone will put your portfolio ahead of 90% of applicants.

If you’re building a portfolio right now, build it like you already have the job — not like you’re trying to pass a course.

I didn’t just use ChatGPT this year.
I built systems with it.

What started as late-night prompts to sharpen SQL explanations and tighten LinkedIn hooks quietly became something bigger:

• Repeatable coaching frameworks
• Clearer storytelling that actually converts
• Faster ways to turn experience into student outcomes

This carousel is my 2025 wrapped, but not in the vanity-metrics way.

It’s about what changed in how I work:

I stopped treating expertise like effort.
I started treating it like infrastructure.

A few lessons that stuck with me this year:

– Teaching scales when you design for outcomes, not syntax
– Storytelling isn’t fluff, it’s a force multiplier for trust
– AI works best as a co-pilot, not a replacement
– Momentum comes from deciding, then iterating in public

The biggest surprise?

The posts I almost didn’t publish.
The drafts that felt unfinished.
The ideas I shared before they felt “ready.”

Those are the ones people remembered.

If you’re building in data, coaching, or education right now, my encouragement is simple:

Don’t just work harder.
Design systems that carry your impact forward.

👇
If one slide or lesson resonated, comment NAVIGATOR and tell me which one.

Onward.

Year in ChatGPT + Lessons for Builders

If you are not using AI to build your datasets for your projects, you're missing out on a lot!

Build your data-portfolio projects that solve real organizational goals by using public datasets and AI to tweak them into realistic, interview-ready case studies.

Make the data mirror real world data with all it's nuances and data quality issues.

The future of work isn’t arriving all at once.

It’s showing up quietly in how teams are managed, how decisions are made, and how value is measured.

As I read HBR’s latest take on the trends shaping work in 2025 and beyond, one thing stood out clearly:

The biggest shifts aren’t technological. They’re behavioral.

Here’s what leaders and professionals are going to have to wrestle with next:

• Work is becoming more outcome-driven than role-driven
• AI is moving from “productivity tool” to “decision partner”
• Careers are less about ladders and more about portfolios
• Trust, judgment, and adaptability are becoming premium skills
• Organizations are rethinking what “performance” actually means

What struck me most is this:
The advantage won’t go to those who learn the most tools.
It’ll go to those who learn how to think better in ambiguous environments.

For data professionals especially, this changes the game.

Your value won’t just come from dashboards, models, or pipelines.
It’ll come from your ability to: • Frame the right questions
• Challenge assumptions with evidence
• Translate insight into action
• Partner with the business, not just support it

The future of work is less about doing more.
It’s about doing what matters, intentionally.

Curious how you’re preparing for the next version of work.
Are you doubling down on skills, judgment, or leverage?

#52DaysDataChallenge

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