EVP & GM NWS North America | LinkedIn Top Voice | Award Winning Author of Never Sit in the Lobby🏆 | Podcast Host | Business Coach | Public Speaker | Helping people sell & business grow 10x

I’ve spent over four decades building, leading, and scaling distribution businesses in test and measurement.

My career includes founding mmWave Technologies in 1991, serving as President of Anritsu Electronics Canada, and building Gap Wireless from a startup to a multimillion-dollar distributor. Gap Wireless was acquired in 2022, after which I led Canadian operations for NWS through the end of 2024.

Today, I am President of ProgUSA, a U.S.-based distributor serving power utilities and service firms with electrical test and measurement equipment. The business focuses on substation, transmission, and distribution applications, supported by a service and calibration center, and a technical support team.

I’ve spent years in the field and in leadership roles, working with manufacturers, customers, and technical teams. My focus is practical execution, strong relationships, and building durable businesses that customers trust.

I’m also the author of Never Sit in the Lobby, a practical book on sales execution, and I regularly share lessons learned through speaking and podcast appearances.

I welcome conversations with utility professionals, manufacturers, and teams focused on long-term growth in the power sector.

Glenn Poulos

Security Data Engineer @Just Eat Takeaway| Ex-bigspark, PayU| Mentor @ Learnbay| Women Health | Mental Health

I bridge two worlds: data engineering and developer advocacy.
I’ve spent years building secure and scalable systems at companies like Ford, Just Eat, and PayU, and I’ve learned that the real value comes from sharing those lessons with others. That’s why I use my LinkedIn platform to connect with CTOs, founders, and dev teams, making complex topics simple and actionable.

What sets me apart is that I’m not just speaking about theory. My work is rooted in hands-on engineering, which means the stories and insights I share are tested in real-world environments.

Alongside my engineering role, I create content and conversations for developers, founders, and technical leaders—on LinkedIn, in podcasts, and at events. My focus remains the same: to make complex technical topics clear, relevant, and actionable.

I also collaborate with brands, startups, and event organisers who want authentic voices to connect with developers. Whether it’s representing a company as an advocate, creating thought leadership content, or speaking at industry events, I aim to bring credibility and trust to every partnership.

Looking ahead, I am growing my presence as a tech evangelist and speaker in the B2B and developer-first space—building meaningful connections with leaders and communities that share the same commitment to clarity, trust, and innovation.

If you are a brand, founder, or event organiser seeking a credible and authentic voice at the intersection of engineering and advocacy, I would be glad to connect.

Disha Mukherjee

Everyone is rushing to become AI-first.
But most teams forget one thing.
AI sits on top of data infrastructure.

And if that foundation is weak, everything above it collapses.
From a data engineer’s perspective, the real order is:

Data → Pipelines → Quality → Governance → AI
Not the other way around.

When companies go AI-first without fixing their data layer, they face:
• Broken pipelines
• Poor data quality
• Hallucinated insights
• Unreliable models

It’s like building a luxury building on rotting pillars.

The companies winning with AI today did something different.

They went Data First.

They invested in:
• Reliable pipelines
• Strong data modeling
• Observability
• Data governance
• Scalable platforms

Only then did AI become powerful.

Because AI is only as good as the data beneath it.

For data engineers, this isn’t new.

We’ve always known:
Data infrastructure is the real foundation of AI.

💬 Curious to hear from other data engineers:

Do you think companies should go AI-first or Data-first?

If you found this helpful,
 🔁 Repost to help someone choose the right path.
 📌 Follow for practical insights on data careers and systems thinking.

📩 Subscribe to my Newsletter to get deep dives on data engineering, system design, and AI infrastructure - https://lnkd.in/eFPw_cd5

#DataEngineering #AI #DataPlatform #DataInfrastructure #DataQuality #MachineLearning

At some point in your data career, you realize something uncomfortable:

Being busy is easy.
Being impactful is rare.

I’ve seen teams celebrate:

Shipping 12 dashboards in a quarter.
Migrating to a new data stack.
Closing every Jira ticket in the sprint.
Deploying yet another model to production.

On paper, it looks impressive.

But then you ask one simple question:

“What changed because of this?”

Silence.

No decision improved.
No cost reduced.
No revenue influenced.
No behavior shifted.

That’s when it clicks.

Data work has two layers:

Layer 1 → Build artifacts.
Layer 2 → Change outcomes.

Most professionals optimize for Layer 1.
The best ones design for Layer 2.

A dashboard isn’t success.
A better decision is.

A model isn’t impact.
Adoption is.

A pipeline isn’t value.
Reliability that unlocks trust is.

If you want to become essential — not just employed — start measuring differently:

• Time-to-decision
• Decision quality
• Adoption rate
• Business movement
• Trust in data

Outputs keep you busy.
Outcomes build influence.

So here’s the real question:

When your work ships…
what actually moves?

Curious what’s the one metric you personally use to define success in data?

📩 Subscribe to my Newsletter to get deep dives on data engineering, system design, and AI infrastructure - https://lnkd.in/eFPw_cd5

#DataEngineering #DataLeadership #BusinessImpact #AnalyticsStrategy #DataDriven #TechCareers

I recently finished reading 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐥𝐥𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐝 by Maaike van Putten and Imke van Putten and I wanted to share a few thoughts.

As a cat person, I’ll admit…

The illustrated format immediately caught my attention 🐾

You don’t often see technical books lean this intentionally into visual storytelling. It’s very different from the typical dense, 500-page programming manual.

And that’s exactly the point.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐨𝐨𝐤 𝐝𝐨𝐞𝐬 𝐰𝐞𝐥𝐥
It’s clearly designed for:
• Beginners
• Visual learners
• People who feel overwhelmed by traditional coding books

𝐈𝐭 𝐰𝐚𝐥𝐤𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:
→ Environment setup & terminal basics
→ Variables & data types
→ Control flow & loops
→ Collections
→ Functions
→ File handling
→ Object-oriented programming & inheritance
→ Debugging

The structure is sequential and intentional.

Each chapter builds on the previous one.
And importantly it includes exercises and quizzes.

Because learning to code isn’t about reading.

It’s about doing.
What I appreciated most
The tone is light.

But the technical explanations are accurate and aligned with modern Python 3 practices.

And debugging is treated as a skill in its own right not just a footnote.

That matters.

Most beginners struggle not because syntax is hard…
But because they don’t know how to think when something breaks.

𝐖𝐡𝐨 𝐭𝐡𝐢𝐬 𝐛𝐨𝐨𝐤 𝐢𝐬 (𝐚𝐧𝐝 𝐢𝐬𝐧’𝐭) 𝐟𝐨𝐫
This is not a book about:
• Production-scale engineering
• System design
• Data architecture

It’s a fundamentals book.

It helps someone build clarity.
Confidence.
And mental models.

And honestly, we underestimate how important that stage is.

Strong fundamentals create strong engineers.

If you’re new to programming  or you learn better visually than through long blocks of text  this is worth exploring:
🔗 https://lnkd.in/excTGBSj

#Python #Programming #DataEngineering #TechBooks #LearningToCode #WomenInTech

Powering the Future of Data Engineering with the Cloud ☁️

Modern data engineering isn’t just about pipelines anymore.

It’s about scalable systems, real-time insights, and reliable data platforms that support everything from analytics to AI.

Today, the big three cloud platforms — Azure, AWS, and Google Cloud — provide a rich ecosystem of tools that power the entire data lifecycle.

From data ingestion → processing → analytics → governance → visualization.

Here’s a simple way to think about how cloud platforms support data engineering.

1️⃣ Storage & Data Lakes

Every data platform starts with storage.

Azure → Azure Data Lake
AWS → Amazon S3
GCP → Cloud Storage

These services provide scalable, durable storage for structured and unstructured data.

2️⃣ Data Pipelines & Processing

Once data is stored, it needs to be moved and transformed.

Azure → Azure Data Factory
AWS → AWS Glue
GCP → Dataflow

These tools help engineers build ETL/ELT pipelines and automate data movement across systems.

3️⃣ Big Data & Analytics

This is where data becomes insight.

Azure → Azure Synapse Analytics
AWS → Amazon Redshift
GCP → BigQuery

They allow teams to run large-scale analytical queries across massive datasets.

4️⃣ Real-Time Data Processing

Businesses increasingly need insights as events happen.

Azure → Stream Analytics
AWS → Kinesis
GCP → Pub/Sub

These services enable real-time pipelines for streaming data.

5️⃣ Visualization & Business Intelligence

Finally, insights must be accessible.

Azure → Power BI
AWS → QuickSight
GCP → Looker

These tools transform raw data into interactive dashboards and business insights.

What’s fascinating is that while each cloud has different tools, the underlying data engineering patterns remain the same.

Storage → Processing → Analytics → Visualization.

The platform changes.
The engineering thinking stays consistent.

And that’s the real skill data engineers build.

If you're learning data engineering, start by understanding the architecture patterns first the tools will follow.

Which cloud platform do you work with the most: Azure, AWS, or GCP?

#DataEngineering #CloudComputing #DataArchitecture #BigData #Analytics #TechLearning

What a night at the 𝐀𝐥𝐥𝐢𝐞𝐬 𝐢𝐧 𝐖𝐨𝐦𝐞𝐧 𝐢𝐧 𝐃𝐚𝐭𝐚 & 𝐀𝐈 𝐞𝐯𝐞𝐧𝐭. 

The room was full, the conversations were thoughtful, and the energy from the community was amazing to see. 

A huge shoutout to the 128 people who attended and made the discussion so engaging.

One theme came up again and again during the panel:
↳ 𝐓𝐡𝐞𝐫𝐞 𝐢𝐬 𝐧𝐨 𝐬𝐢𝐧𝐠𝐥𝐞 𝐩𝐚𝐭𝐡 𝐢𝐧𝐭𝐨 𝐚 𝐜𝐚𝐫𝐞𝐞𝐫 𝐢𝐧 𝐝𝐚𝐭𝐚.
↳ Every speaker had a different journey.
↳ Some started technical.
↳ Others came from completely different fields.
↳ Yet they all built impactful careers in data.

A few journeys that really stayed with me:
• 𝐉𝐨𝐬𝐢𝐞 𝐖𝐢𝐥𝐥𝐢𝐚𝐦𝐬 shared a more linear path - starting as a DBA, moving into consulting, then BI, and eventually leading teams.
• 𝐇𝐚𝐳𝐚𝐥 𝐌𝐮𝐡𝐭𝐚𝐫 began with no coding background, gradually moved into technical roles, and now works as Senior Director of Product Analytics at Wise.
• 𝐋𝐚𝐮𝐫𝐚 𝐋𝐞𝐰𝐢𝐬 took a very unconventional route - marketing → archaeology → treasury → startups → bootcamp → Amazon (Alexa) → consulting → Monzo — always driven by a desire to create impact.
• 𝐋𝐢𝐧𝐚 𝐌𝐢𝐤𝐨𝐥𝐚𝐣𝐜𝐳𝐲𝐤 initially pursued medicine, but during a gap year discovered a love for numbers while working in operations at an insurance company. That curiosity eventually led her into data roles and leadership after reaching out to alumni for opportunities.

 Different backgrounds.
 Different starting points.
 But one common lesson.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬, 𝐲𝐨𝐮 𝐨𝐟𝐭𝐞𝐧 𝐡𝐚𝐯𝐞 𝐭𝐨 𝐚𝐬𝐤 𝐟𝐨𝐫 𝐭𝐡𝐞𝐦.
The worst possible answer is 𝐧𝐨.

But sometimes asking leads to a conversation, a connection, or an opportunity you didn’t expect.

Another powerful takeaway:
𝐅𝐢𝐧𝐝 𝐲𝐨𝐮𝐫 𝐚𝐥𝐥𝐢𝐞𝐬.
Careers rarely grow in isolation.
They grow through people who support you, advocate for you, and open doors you didn’t know existed.

A few insights from the discussion that really stuck with me:
▫️ Don’t wait until you feel 100% ready. Growth often comes from stepping into opportunities before you feel fully confident.
▫️ Leadership isn’t about knowing everything. It’s about facilitating the conversation and helping teams navigate complexity.
▫️ Clear communication is essential. Being able to explain technical work in a way that business stakeholders understand is a powerful skill.
▫️ Self-doubt is common. Even experienced leaders experience it - the key is not letting it hold you back.
▫️ Relationships matter. Many opportunities come from connections and conversations, not just applications.

A huge thank you to our excellent panel for sharing their experiences:
- Lina Mikolajczyk
- Laura Lewis, PhD
- Hazal Muhtar
- Josie W.

Events like this are a great reminder of how powerful community and allies are in shaping careers in data and AI.

Looking forward to the next one.

#WomenInData #WomenInTech #DataCommunity #WomenInLeadership #DataCareers 🚀

Most people think success is a straight line.

It’s not.

It looks more like this 👇

You start out excited.
Trying to figure things out.

Then reality hits.

You try different paths.
You hit roadblocks.
You fail… and then fail again.

And somewhere in between all that chaos,
you learn the most important things:

→ Showing up even when it’s hard
→ Staying consistent when results are slow
→ Pushing through doubt when nothing makes sense

Because career growth isn’t about avoiding failure.

It’s about learning how to keep going *despite it.*

The people who succeed aren’t the ones who never fail.

They’re the ones who don’t stop.

What stage are you in right now?

#CareerGrowth #Consistency #LearningJourney #Mindset #WomenInTech #BuildInPublic

Most people use AI tools randomly.
That’s the mistake.
Not all AI tools are built for the same job.

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐬𝐢𝐦𝐩𝐥𝐞 𝐰𝐚𝐲 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭 𝐢𝐭 👇
👉 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐡𝐚𝐭
Best when you need *thinking + clarity*
– Writing
– Research
– Brainstorming
– Explaining concepts

You stay in control. AI supports you.

👉 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞
Best when you need *execution in code*
– Refactoring projects
– Running tests
– Working across multiple files
– Automating dev workflows

You define the goal. AI executes.


👉 𝐂𝐨𝐰𝐨𝐫𝐤
Best when you need *execution beyond code*
– File organization
– Report generation
– Cross-app workflows
– Repetitive business tasks

You describe the task. AI delivers outputs.

💡𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 𝐢𝐬 𝐬𝐢𝐦𝐩𝐥𝐞:

𝐅𝐫𝐨𝐦
“Which AI tool is best?”

𝐓𝐨
“Which AI tool fits this task?”

That’s where real productivity begins.

If you had to pick one you use the most right now… which would it be?

If you found this helpful,
 🔁 Repost to help someone choose the right path.
 📌 Follow for practical insights on data careers and systems thinking.

📩 Subscribe to my Newsletter to get deep dives on data engineering, system design, and AI infrastructure - https://lnkd.in/eFPw_cd5

#AI #Productivity  #DeveloperTools  #Automation  #TechTrends  #FutureOfWork Claude

Image credits to respective creator

1 in 10 women worldwide live with endometriosis.
That’s around 190 million women.

Many of them are sitting in meetings right now.
Joining stand-ups.
Leading teams.
Writing code.
Presenting to clients.

And most people around them have no idea what they’re going through.

Endometriosis is not “just bad cramps.”

It can mean:
* Chronic pelvic pain
* Debilitating fatigue
* Brain fog
* Hormonal struggles
* Fertility challenges
* Years of delayed diagnosis

For many women, the pain can be so severe it feels impossible to stand — yet they still show up to work.

They smile through meetings.
Deliver deadlines.
Lead projects.

All while their body is fighting a battle that remains largely invisible.

And often, when they do speak up, they hear things like:
“Maybe it’s just stress.”
“It’s normal period pain.”
“Take a painkiller.”

But the truth is - it’s not normal.

It’s a serious condition that affects not just health, but careers, confidence, and everyday life.

So today, I want to give a shoutout to every woman living and working with endometriosis.

To the women pushing through the pain.
To the women advocating for their health.
To the women who refuse to let this condition define their potential.

Your resilience is extraordinary.

And to workplaces and leaders everywhere:

Let’s build cultures where women feel safe talking about health challenges.

Where empathy is not seen as weakness.

And where invisible illnesses are acknowledged, not dismissed.
Because strength isn’t just showing up when everything is easy.
Sometimes strength is simply showing up despite the pain.

To all the endometriosis warriors out there 
we see you, we respect you, and we stand with you.

❤️

#EndometriosisAwareness #WomenInTech #InvisibleIllness #WomenSupportingWomen #WorkplaceEmpathy #EndoWarrior

I once organised a meeting.
Set the agenda.
Prepared every talking point.
Sent the invite with clarity and intent.
And then, while sitting in that room, I felt it slip away.
Not abruptly. Not aggressively.
But quietly. Smoothly. Almost invisibly.
A small interruption here.
A subtle reframe there.
Until, before I even realised it, my ideas were moving around the room… just not in my voice anymore.
By the end, the conversation had shifted.
The ownership had shifted. And somehow, the meeting no longer felt like mine.
I was still there. Just no longer part of it.

That experience stayed with me. Not as bitterness, but as a question:

What would it have taken for that room to work differently?

I think it comes down to four small habits that cost nothing but intention:

1. Name the idea AND the person, out loud
When a leader summarises a discussion, they should say: "Priya raised this" or "that came from Disha." Not in the notes later. In the room, in the moment. Attribution spoken aloud is attribution that sticks.

2. Protect the unfinished thought
Create one space in every meeting where no one interrupts. Not to correct, not to build on, not to redirect. Just to let the thought land as it was meant to. Many of us never get that.

3. Write it down as it happens
A live shared doc with ideas and names beside them. It is surprisingly hard to take credit for something already in writing, next to someone else's name, in a document twelve people can see.

4. Assign someone to look back
One person per meeting whose job is simply to notice: "Did anything get lost?" And to bring it back. "I want to return to what she said earlier, because I don't think we finished with it."

None of these is radical.

They don't require a policy change or a training budget. They require a leader who is paying attention to more than just the loudest voices in the room.

The best leaders don't just open doors.

They notice who walked through, what they said, and make sure the room heard it too.

#IWD2026 #WomenAtWork #LinkedInNewsUK

Early in her career, Emma Chalwin, the chief marketing officer of Workday, shared an idea in a room full of male executives.

After the meeting, the feedback she received was not about the substance of her idea, but about her tone. She was told she was "too emotional". 

The next day, a male colleague repeated her idea – and was praised for it. 

That experience quieted her voice. She spoke less, until a new manager changed the dynamic. In a meeting months later, when Emma hesitated, that manager pushed the floor back to her: "Emma has a really excellent point of view," she said. "She's a subject matter expert in this area and I'd love for her to share her perspective."

Sometimes leadership is simply making sure the right voice is heard.

🤔 What's a constructive way to push back when people are labelled 'too emotional' at work and what can workplaces do to ensure women's ideas are heard?

Watch the video below and join the conversation.

#IWD2026 #WomenAtWork

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Disha Mukherjee

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Disha Mukherjee's LinkedIn Performance

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