Redfish Technology

My role encompasses recruiting top-notch candidates for our clients, overseeing multiple recruiting teams, and driving strategic business development initiatives at Redfish Technology. We specialize in recruiting talented professionals within the tech industry, spanning across sales, marketing, engineering, and customer success domains. Collaborating with startups nationwide, we excel in rapidly and effectively building out their teams.

Established in 1996, we pride ourselves on upholding the highest standards of integrity, excellence, and performance in recruitment. Personally, I am deeply passionate about technology and dedicated to attracting the best and brightest talent to contribute to our clients' success.

Specializing in  Edtech, Fintech, AI, Enterprise Software, Ad tech, Big Data, Security, Mobile, SaaS, Wireless, E-Learning, CRM, Marketing Automation, Computer Vision and Networking.

Chief Revenue Officer

As the leader of a dynamic 6-person team, we specialized in sourcing rare talent within the tech sector. 

Recognized as a 2011 top Executive Search Firms of the U.S. by Leading Providers

Managing Director

Executive Recruiter, High Tech Talent Division

Chaparral Broadcasting

Chaparral Broadcasting 
Kech 95.3, KSKI 103.7, KYZK 107.5, KMTN 96.9, KSGT 1340 AM, KJAX 93.3, KZJH 95.3
 
•	Sold advertising and marketing plans across 6 radio stations
•	#1 Account Executive in hunting new business and building new territories
•	Consistently in top 5 sales reps across the company
•	Clients included advertising agencies and direct buys from businesses
•	Managed existing accounts, met with clients and prepared marketing campaigns
•	Inserted ad schedules on a daily basis
•	Built an account base from zero
•	Account management of existing clients
•	Managed multiple marketing campaigns and coordinated remote events

Account Executive

CRO @ Redfish Technology || Providing Disruptive Tech Companies w/ Talent || 24k Followers || Forbes Next 1000 || Girl Mom

Forbes 2021 - Next 1000 Honoree


Chief Revenue Officer at Redfish Technology, where I lead with empathy, strategic insight, and a commitment to building strong relationships. 

As Viola Davis once said, "My dreams are bigger than my fears."

Our Work

Helped a new VP of Sales at an Enterprise AI Client hire the first 3 members of his Sales Team: Our team consulted with the VP to identify the profiles he needed, researched and built a list of target companies to source from, then reached out nationwide to over 350 AEs in our network, interviewed/screened over 70, submitted 30 to the VP for review, and he hired 3 - saving 100’s of man-hours in the process.

We've built out sales and engineering teams with the top 30 Edtech startups across the country.

Placed Director of Growth at a Fintech Startup: After hiring the Director of Growth, we were asked if we could help with two PM roles, they were struggling with filling. We did so in about 5 weeks for both a Platform PM and Mobile PM. This led to an introduction to their Design team, where we have helped them hire 3 Sr. Product Designers.

Leah Scarnegi

If you’ve identified a rockstar candidate, chances are others have too. 
 
Imagine this: You’re in need of a Salesperson and find the perfect fit. But your hiring process drags. By the time you’re ready to make an offer, they’re already onboarded somewhere else. A missed opportunity! 
 
Delays in hiring aren’t a sign of being thorough—they’re a risk. The longer your process, the more likely top candidates will slip through your fingers. 
 
To stay competitive: 
 
Streamline resume reviews 
Speed up interview scheduling 
Make offers promptly 
Don’t lose the perfect candidate due to unnecessary delays. Move fast, hire smart, and secure the best talent for your team! 
 
#RecruitmentStrategy #HiringTips #TopTalent #EfficiencyMatters #DontMissOut

Leah Scarnegi 

CRO @ Redfish || Scaling Startups with the Right People || Blaine County Board of Education || Forbes 2021 - Next 1000 Honoree

How to Ask for More Money When Negotiating an Offer

Negotiating an offer can feel uncomfortable—but it doesn’t have to be. One simple shift can make all the difference:

 you’re asking for more, clearly state that you’ll commit if the company meets your request.

Too often candidates say, Can we increase the salary? without signaling that saying yes gets the deal done. This leaves hiring teams unsure whether the ask will actually close the loop—or if they’ll approve the increase only to learn the candidate is still undecided.

Here’s a better approach:

Be specific about what you need
“I’m very excited about the role. If we can adjust the base to $X, I’m ready to sign.”

Show you’re serious.
Linking your ask to a commitment builds trust and reduces risk for the employer.


Negotiation isn’t about being demanding—it’s about alignment. When you make it clear that meeting your request = you’ll accept the offer, you streamline the process and strengthen your position.

#joboffers #jobsearch

Want Faster, Smoother Hiring? Fix the Intake Meeting.

When I talk about an *intake meeting*, I mean something very specific:

The kickoff call between the recruiter and the hiring manager where the search is defined, aligned, and set up for success.

It’s not a casual chat.
It’s not a repeat of the job description.
It’s the strategic planning session that determines whether the search will run smoothly or fall apart later.

But here’s the issue:

Only a portion of intake meetings actually cover BOTH the decision flow and the criteria — and that’s where delays, misalignment, and lost candidates begin.

Here’s what should happen in an intake meeting:

1. Decision Flow

This is where you map out:

* Who interviews
* In what order
* What each interviewer is responsible for assessing
* Who makes the final call
* How fast decisions can be made

Without this, you end up with last-minute panel changes, inconsistent feedback, and stalled candidates.

2. Offer Criteria

This includes:

* Salary range
* Equity/bonus structure
* Approval process
* Timeline to extend an offer
* Where the team has flexibility (or not)

If this isn’t aligned upfront, you hit friction at the finish line — or lose candidates to faster teams.

www.redfishtech.com

#hiring #recruiting #hiringtips

Open article: IT & Tech Recruiters and Executive Search | Redfish Technology by redfishtech.com

IT & Tech Recruiters and Executive Search | Redfish Technology

The Resume Format That Actually Helps Gets Engineers Hired (Part 3)

Your skills section is giving you away.

Every engineer lists Python, React, Docker, AWS.
But the strongest resumes do something different:

✅ 1. Keep Skills Tight & Honest

Only list tech you can talk about for 30 minutes without sweating.
A tight list reads senior.
A bloated list reads desperate.

✅ 2. Show How You Used the Tech

This is the real differentiator.

Instead of a laundry list, your experience should show application:

"Deployed LLM pipelines using LangChain + Ollama on GPU instances"

“Built distributed ransomware detection using Kafka + PostgreSQL + Spring Boot”

“Architected CI/CD for Airflow + PostgreSQL using Jenkins & Docker”

Your stack isn’t just what you know — it’s how you apply it.

#resumetips #engineering #resume

Hiring Managers: How to Spot a Quality Recruiter

Not all recruiters are created equal — and the right one will save you time, elevate your hiring bar, and protect your brand.

Here’s how to screen for quality:

✅ They ask smart, detailed questions in the intake.
If they’re not digging into decision flow, offer criteria, success metrics, and deal-breakers, they’re not setting you up for a smooth search.

✅ They push back (professionally).
A great recruiter won’t just take orders — they’ll challenge unclear requirements and help you refine what you actually need.

✅ They communicate consistently and transparently.
No guessing games. No disappearing. They set expectations and hold everyone accountable — including you.

✅ They present fewer candidates, but better ones.
Quality recruiters curate, not spam. Every candidate should feel intentional.

If a recruiter does these four things, you’re in good hands. If not... keep looking.

www.redfishtech.com

#hiring #recruiting #hiringmanager

The Resume Format That Actually Gets Engineers Hired (Part 5 — Final)
This is how you get read in 6 seconds.

✅ 1. Make It Scannable

Hiring managers skim — they don’t read.
Your resume should use:

consistent formatting

short bullets

white space

bolded roles/companies

no colors, text boxes, or templates that break ATS

Clear beats clever. Every time.

✅ 2. Keep It to One Page (Unless You’re Highly Senior)

Under 15 years experience? One page is perfect.
Over 15 years or deep research/architecture work? Two pages is totally fine.

✅ 3. Show Seniority Through Ownership

Seniority isn’t years — it’s impact.
The strongest resumes highlight:

Systems you designed

Pipelines you architected

Teams you mentored

Migrations you owned

Scale, performance, reliability, security

Ownership > job title. Always.

#resumes #engineering

The Resume Format That Helps Gets Engineers Hired (Part 1)

What every engineer should fix today.

If you want a recruiter or hiring manager to read your resume — start with the basics that almost everyone gets wrong:

✅ 1. 𝙺𝚎𝚎𝚙 𝚝𝚑𝚎 𝙷𝚎𝚊𝚍𝚎𝚛 𝚂𝚒𝚖𝚙𝚕𝚎

No paragraphs. No summaries. No mission statements.
Just:

Name

Email

Location (or “Remote”)

LinkedIn

GitHub

Portfolio (optional but 🔥)

Clean. Scannable. Professional.

#resumetips #resume #engineering

The Resume Format That Actually Helps Engineers Hired (Part 2)

This is the difference between forgettable and unforgettable.

Most engineers write bullet points that look like:
❌ “Worked on APIs”
❌ “Responsible for front-end performance”

These aren’t helping you.

✅ Instead: Use This Formula

Strong verb → what you built → the tech you used → measurable impact

Examples from real placed candidates:
✔ Optimized API endpoints to handle 20,000+ TPS with 175ms latency
✔ Built peak detection algorithms with >99.9% accuracy
✔ Processed millions of assets in under 10 seconds

Numbers = credibility.
Impact = seniority.
Specificity = interviews.

#resumetips #engineering #resume

The Resume Format That Actually Helps Gets Engineers Hired (Part 4)
Your projects section can elevate you... or expose you.

Strong projects follow the exact same structure as your work experience:

- What you built
- The technologies you used
- The outcome or impact

Examples from real placed candidates:
✔ Built a cloud encryption app in Rust + Tauri used for managing S3/Google Drive files
✔ Developed a course planner using a custom topological sort algorithm
✔ Built a video-search CV tool using OpenCV + PyTorch + SAM

Projects should prove engineering judgment — not just that you completed assignments.

#resume #engineering #resumeformat

Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

I am excited to share a comprehensive resource: Machine Learning, Deep Learning, and Artificial Intelligence Cheat Sheets compiled by Nikhil Yadav from Stanford University and MIT!

This collection of cheat sheets covers key concepts and techniques across machine learning, deep learning, and AI. 

Perfect for students, professionals, and enthusiasts, this document provides quick references and summaries of essential topics in these fields.

Key topics include:
- Probability and Statistics fundamentals
- Supervised and Unsupervised Learning algorithms
- Linear and Logistic Regression
- Support Vector Machines (SVM) and Generative Learning
- Tree-based and Ensemble Methods
- Principal Component Analysis (PCA) and other dimension reduction techniques
- Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
- Reinforcement Learning and Control
- Essential metrics for model evaluation and selection
- Tips and tricks for effective machine learning practice


Enhance your understanding and mastery of machine learning, deep learning, and AI with these detailed cheat sheets!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

ML, Deep Learning and AI Cheat Sheet

Excited to share an essential resource: DAX Query Plans by Alberto Ferrari!

This comprehensive paper introduces the performance analysis and optimization of DAX code through the use of DAX query plans. 

Ideal for BI professionals and data analysts, this document provides valuable insights into improving query performance by understanding and optimizing DAX queries.

Key topics include:
- Finding and interpreting DAX query plans
- Differences between logical and physical query plans
- Overview of the formula engine and storage engine
- Basic optimization techniques for DAX queries
- Practical examples using the Contoso and AdventureWorks databases


Enhance your DAX skills and improve query performance with this detailed guide!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

li_at

JSESSIONID

Talks about #data, #kindness, #interviewtips, #selfimprovement, and #careertransition

DAX Query Plans

Excited to share an essential resource: DevOps Interview Questions!

This comprehensive guide covers a wide range of questions that are commonly asked in DevOps interviews. 

Whether you're a beginner or an experienced professional, this document provides valuable insights and answers to help you ace your interview and enhance your DevOps knowledge.

Key topics include:
- Understanding DevOps and its need in the industry
- Top DevOps tools and their uses
- Differences between DevOps and Agile/SDLC
- Core benefits and advantages of DevOps
- Key performance indicators (KPIs) for gauging DevOps success
- Role of configuration management and popular tools like Ansible
- Concepts like "Shift left to reduce failure," Blue/Green Deployment, and more
- In-depth look at DevOps methodologies and real-life use cases
- Detailed explanations of tools like Docker, Kubernetes, Jenkins, and AWS


Enhance your interview preparation and stay ahead in the competitive DevOps field with this detailed guide!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

DevOps Interview Questions

I'm excited to share an essential resource: Python Notes/Cheat Sheet by Mark Graph!

This comprehensive cheat sheet covers a wide range of Python concepts, providing quick references and summaries for both beginners and experienced developers. 

It's an invaluable resource for anyone looking to enhance their Python programming skills.

Key topics include:
- Basic syntax and naming conventions
- Data types: integers, floats, strings, lists, tuples, sets, and dictionaries
- Operators and their functionalities
- Flow control statements: if, for, while, and try/except
- Functions and lambda expressions
- Modules and import statements
- Object-oriented programming: classes, methods, and inheritance
- Commonly used libraries and modules: datetime, math, re, numpy, pandas, matplotlib
- Advanced concepts: iterators, generators, and decorators


Enhance your Python programming knowledge and boost your productivity with this detailed cheat sheet!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

Python Notes

Why data engineers are like the unsung heroes of the tech world – and why you should become one.

Ever wondered what makes the tech world tick?

It’s not just the flashy apps or cutting-edge AI.
It’s the data engineers behind the scenes, making everything work smoothly.

Here’s why they’re the real MVPs:

1. Foundation Builders

Data engineers lay the groundwork for any data-driven project.

Without them, data scientists and analysts would be flying blind.

They create robust data pipelines that ensure seamless data flow – the backbone of every tech solution.

2. Problem Solvers

Every day brings a new challenge.

From fragmented data sources to integrating complex systems, data engineers thrive on solving intricate puzzles.

Their work is crucial for turning raw data into actionable insights.

3. Innovation Enablers

The world’s first AI system for air traffic control?

That’s just one example.

Data engineers make groundbreaking innovations possible by ensuring data is clean, reliable, and ready for analysis.

4. Guardians of Data Integrity

Data is valuable, but only if it’s accurate.

Data engineers ensure quality and consistency, protecting organisations from costly errors.

They’re the gatekeepers of trustworthy data.

5. Educators and Mentors

Building the future doesn’t stop at technology.

Data engineers also shape the next generation of tech talent, sharing their knowledge and experience with aspiring professionals.

So, why should you consider becoming a data engineer?
→ It’s a role that combines creativity and technical skill.
→ There’s an evergrowing demand for data-driven solutions.
→ You’ll be at the forefront of innovation, where every project is an opportunity to make a significant impact.

Join the ranks of these unsung heroes and be part of a community that drives the tech world forward.

Are you ready to build the future?

Let’s discuss why data engineering should be your next step.

Drop a comment below to share your thoughts or ask questions about this exciting field.

Data degrees are great, but here’s why data engineering skills might just be the game-changer you need.

In a world overflowing with data, what’s the biggest challenge?

Turning it into something valuable.

That’s where a data engineer steps in.

Here’s why mastering data engineering skills can outshine a traditional data science degree in today’s job market:

1. Demand for Data Engineers is Soaring

→ Companies need robust data pipelines and clean data before they can even start analysing.

↳ Without data engineers, data scientists can’t do their jobs effectively.

2. HandsOn Problem Solving

→ Data engineering focuses on building and optimising data systems.

↳ You’ll deal with realworld data challenges and provide solutions that have immediate impacts.

3. Versatile Skill Set

→ From coding and database management to understanding cloud services, data engineering equips you with a wide array of skills.

↳ It’s about the whole data ecosystem, not just analysis.

4. Foundation for Advanced Data Exploration

→ Before models can be trained, data must be prepped and organised.

↳ This means your work sets the stage for any advanced data science application.

5. Bridge the Gap Between Theory and Practice

→ While data science often leans towards theoretical aspects, engineering is grounded in practical application.

↳ You’re not just predicting the future; you’re building the road to it.

6. CostEffective Learning

→ You can learn data engineering through online courses, handson projects, and bootcamps.

↳ No need for a hefty degree investment.

7. Innovation at the Core

→ Data engineers are the architects of the digital age, creating systems that allow companies to innovate and grow.

↳ You’re paving the way for future technologies.

So, if you’re weighing options between a degree and diving into data engineering, consider what aligns with the current industry needs.

The world needs more data engineers.

Are you ready to be one?

Engage with me in the comments if you’re considering a career shift or just starting out in data engineering.

What’s your next step?

I was once told that learning Python was the key to unlocking a career in data.

But here's the truth: It's not just about the language.

It's about developing an analytical mindset.

When I transitioned from being a data analyst to a data engineer, I realised the importance of how I approached problems, not just the tools I used.

Here's why:
Understanding Context: Before diving into coding, understanding the problem is crucial.

What are you trying to solve?

What data do you need?

The ability to ask the right questions is far more valuable than knowing syntax.
Critical Thinking Over Code: Coding is a means to an end.

The real skill lies in analysing data and drawing meaningful conclusions.

Can you see patterns others miss?

Can you make sense of the chaos?
Data Literacy: It's not about how many programming languages you know.

It's about understanding data itself.

How do you clean it?

How do you ensure it's reliable?
Communication: Data insights are useless if they can't be communicated.

Can you translate complex data into simple narratives?

Can you make stakeholders understand the impact?
Adaptability: The world of data is ever-evolving.

New tools, new methods, new challenges.

Being open to learning and adapting is key.

So, if you're thinking of diving into data, remember this:

→ It's not just about Python or any other tool.

↳ It's about fostering an analytical mindset.

And that mindset is what will truly set you apart in the world of data.

What do you think?

Are you ready to start thinking differently?

The shocking truth: Your data pipeline is only as strong as the weakest intern who maintains it.

Sound alarming?

It should.

In the world of data engineering, where precision and reliability are nonnegotiable, the integrity of your pipeline rests heavily on every individual involved.

Yet, we often overlook the importance of nurturing and empowering those at the beginning of their journey—the interns.

Here's how to strengthen your pipeline by investing in your interns:
Comprehensive Onboarding

→ Ensure that interns receive thorough training on your systems.

↳ This isn't just about technical skills.

It's about understanding your organisation's data philosophy and goals.
Mentorship

→ Pair interns with experienced data engineers.

↳ Real-world guidance and feedback are invaluable.
Safe Learning Environment

→ Create a culture where mistakes are seen as learning opportunities, not failures.

↳ Encourage questions and exploration.
Clear Responsibility

→ Define their roles and responsibilities clearly.

↳ Clarity prevents errors and builds confidence.
Regular Check-ins

→ Schedule regular meetings to discuss progress and challenges.

↳ Continuous feedback loops help in identifying issues early.
Celebrate Achievements

→ Recognise and celebrate their contributions.

↳ Acknowledgement boosts morale and motivation.

By empowering your interns, you're not just securing your data pipeline.

You're investing in the future leaders of your data teams.

Remember, a robust pipeline isn't built on code alone.

It's built on a foundation of well-trained, engaged, and motivated individuals.

So, next time you review your data processes, ask yourself:

Have you given your interns the tools they need to succeed?

Your pipeline depends on it.

What strategies have you implemented to support your interns?

Share your thoughts!

Want to lead in data? Stop chasing AI buzzwords and start mastering the art of data storytelling.

In the world of data, AI is often the star of the show.

But behind every algorithm is a story waiting to be told.

When I first entered the data realm, like many, I was dazzled by the allure of machine learning and AI.

I thought mastering these would be my ticket to success.

Turns out, I was only half right.

Here's why storytelling matters more than ever:
Stories Resonate
 ↳ Numbers are powerful, but stories connect.
 ↳ They make data relatable.

Simplicity Triumphs
 ↳ Complex algorithms can be intimidating.
 ↳ Simplifying them into a narrative makes your insights accessible.

Actionable Insights
 ↳ Stories guide decisions.
 ↳ They show the impact of data beyond the numbers.

Bridges the Gap
 ↳ Not everyone speaks the language of data.
 ↳ Stories translate insights across diverse audiences.

During my journey, I realised:

Data storytelling isn't just a skill.

It's an art form that demands creativity and empathy.

When I designed and developed a common data platform, it wasn't just about integration.

It was about creating a seamless narrative of connectivity and efficiency.

At NATS, the AI system for air traffic wasn't just a project.

It was a story of innovation and safety.

As I consulted for industry giants, I learned the power of weaving data into a narrative that drove action and transformation.

So, if you're aiming to lead in data:

→ Start with the story.

→ Make your data speak.

→ Inspire those around you.

Don't just be a data professional.

Be a storyteller.

How do you incorporate storytelling into your data projects?

taplio

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Dr Emmanuel Ogungbemi

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The Battle

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Dr Emmanuel Ogungbemi

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