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'm excited to share an invaluable resource: the Data Engineer Interview Guide by Simplilearn!

Preparing for a data engineering interview can be daunting, but this comprehensive guide has got you covered. 

Whether you are a fresh graduate or an experienced professional, this document provides a detailed overview of the key questions and answers you can expect in a data engineering interview.

Key topics include:
- Understanding the role and responsibilities of a data engineer
- Differences between data engineers and data scientists
- Key skills required for data engineering, including SQL, Hadoop, and Python
- Essential frameworks and applications for data engineers
- Commonly asked interview questions and how to answer them
- Practical tips for handling job-related crises and real-world scenarios
- Insights into data modelling, ETL processes, and working with big data in cloud environments


Enhance your interview preparation and stand out from the competition with this detailed guide!

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- Save to revisit down the road
- Add your comment!

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

Data Engineering Intro

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

SQL Introduction Handnotes

The real secret to becoming a data professional isn't technical skills—it's storytelling.

Here's how narrative trumps numbers.

When I first entered the world of data, I was captivated by the endless possibilities that numbers could unlock.

But soon, I realised something crucial: data alone doesn't speak.

It's the story you weave around it that truly makes an impact.

Let me explain how you can harness the power of storytelling in data:

1. Understand Your Audience
  ↳ Tailor your narrative to resonate with the specific needs and interests of your audience.
  Whether you're speaking to executives or data enthusiasts, adjust your story accordingly.

2. Find the Human Element
  ↳ Data is ultimately about people.
  By highlighting the human impact, you make your insights relatable and actionable.

3. Create a Compelling Narrative Arc
  ↳ Like any good story, your data-driven narrative should have a clear beginning, middle, and end.
  Start with the problem, introduce the data, and conclude with actionable insights.

4. Simplify Complex Data
  ↳ Great storytelling involves breaking down complex data into understandable and engaging segments.
  Use visuals and analogies to make your points clear.

5. Inspire Action
  ↳ Your story should lead to a call to action, encouraging your audience to make informed decisions based on your insights.
  
Remember, data storytelling isn't just about presenting facts—it's about driving change.

By mastering the art of storytelling in data, you transform from a mere analyst to an influential data storyteller.

And the best part?

Anyone can learn this skill.

So, are you ready to craft stories that drive data-driven decisions?

Start weaving your narrative today.

Excited to share an insightful resource: MLlib: Scalable Machine Learning on Spark by Xiangrui Meng and collaborators!

This comprehensive guide explores the powerful capabilities of MLlib, Spark's scalable machine learning library.

It covers everything from basic algorithms to advanced machine learning techniques, making it an essential resource for data scientists and engineers looking to leverage Spark for large-scale data processing and analysis.

Key topics include:
- Overview of MLlib and its integration with Apache Spark
- Machine learning algorithms: classification, regression, clustering, and collaborative filtering
- Practical examples and implementations of logistic regression, k-means clustering, and ALS
- Performance comparisons and scaling properties of MLlib
- New features and updates in MLlib v1.x
- Integration with other Spark components like Spark SQL, Streaming, and GraphX


Unlock the full potential of machine learning with MLlib on Spark and take your data projects to the next level!

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- Save to revisit down the road
- Add your comment!

Why I believe the future of data engineering lies not in new tech but in teaching empathy and communication.

I started in data engineering with the belief that the latest tech would solve every problem.

That mindset shifted.

Here's why: it's not just about the tools.

It's about understanding the humans behind the data.

In my journey from data analyst to data engineering leader, I've learned that the hardest problems aren't technical.

They're human.

When I was tasked with transforming raw data into insights, I quickly realised that many organisations were drowning in fragmented data.

Not because they lacked technology, but because they lacked communication.

→ Data was siloed.

→ Teams didn't talk to each other.

→ Processes were inefficient.

So I focused on:

1. Building Bridges: I brought together diverse teams, ensuring everyone understood the data's value and purpose.

2. Teaching Empathy: I encouraged my team to see data not just as numbers but as stories with human impact.

3. Enhancing Communication: I led efforts to develop clear channels and languages for data discussions, making complex insights accessible to all.

It wasn’t just about algorithms.

It was about collaboration.

About understanding the fears and hopes of those who rely on the system.

When we embrace empathy and communication, we transform data from a static resource into a dynamic force for change.

We give it life.

The future of data engineering won't be about the flashiest tools.

It will be about the human touch.

So here's my challenge to you:

Focus on the human side of data.

Ask more questions.

Listen more.

Let's build a future where data brings us together, not keeps us apart.

What do you think?

How can we make data engineering more human?

Share your thoughts below.

Ferenc Fekete

Helping non-tech Founders transform Ideas into SaaS products @ VeryCreatives | Product Agency, SaaS & MVP Development | Startups

Most non-technical founders struggle turning their SaaS idea into a scalable product.

They waste months (or even years):
→ Trying to hire the right developers
→ Burning cash on poor planning and execution
→ Second-guessing every technical decision along the way

Meanwhile, competitors move faster, investors demand traction, and the pressure to deliver keeps building more and more.

I've seen it happen far too many times.
Which is exactly why we started VeryCreatives.

We help early-stage founders go from idea to market-ready MVP in just 90 days...
...all without the stress of managing a development team of your own or writing a single line of code.

For the past 12 years, we’ve been building products for venture-backed startups, fast-growing scale-ups, and even globally recognized brands like Red Bull, Nestlé, and Philips.

Here’s how it works:

👉 1. Clarify your product strategy: 
First, we validate your idea, define your MVP scope, and ensure you’re building something the market actually wants.

👉 2. Create a world-class user experience:
Next, our design team crafts stunning, intuitive interfaces that set your product apart from the competition.

👉 3. Develop a high-quality, scalable MVP:
Finally, we build a robust and investor-ready product that quickly gets you to market (without cutting corners).

The startups we work with have:
✔️ Launched investor-ready MVPs in under 90 days
✔️ Secured funding from top-tier investors and VCs
✔️ Scaled from first users to thousands of paying customers

So if you’re a non-technical founder looking to turn your idea into a fully built MVP in the next 90 days... all without the stress or headache?

Shoot me a DM or book a quick consultation with me here to chat more about how you can get there: https://verycreatives.com/book-a-call

Co-Founder

At VeryCreatives, we are a team of expert developers who create powerful SaaS apps that solve problems and drive growth for founders with initial funding. We believe in empowering our clients with innovative software that helps them achieve their goals and stand out in their industry.

Our audience consists of driven and ambitious founders who have just received initial funding for their startup. They are looking for a software development partner who understands their vision, can help bring their ideas to life, and deliver results that will help them achieve their business goals.

Founders with initial funding have a pressing need to build their product quickly and efficiently to maximize their chances of success. At VeryCreatives, we understand this need and are dedicated to creating high-quality SaaS apps that not only meet their requirements but also exceed their expectations. We are committed to delivering value and results that help our clients succeed.

VeryCreatives stands out in the market for our ability to deliver high-quality, innovative solutions that meet the needs of our clients. Our unique approach to software development, coupled with our expertise and commitment to excellence, is what drives us to provide outstanding results to our clients.

Industries our clients operate in, using the SaaS products we've built: 
- Digital marketing, Event marketing & Influencer marketing SaaS
- Foodtech & Delivery 
- Fintech, Banking & Investments
- 2-sided marketplace platforms
- Agriculture & Livestock management
- Packaging compliance & Waste

Markets we cover: UAE, EU, UK, US.

Last week was tough for us.

One of our team members decided to move on after 9 years.

He joined when his kids were still young. Now they’re almost adults.

Over nearly a decade, he became a core part of the company. A leader. A pillar.

But when he told us he was ready for something new, we completely understood.

Because that's the kind of culture we’ve built here at VeryCreatives.

We talk a lot about creating a workplace where people WANT to stay.

But we also want it to be a place they can leave — on good terms, with support, and without regrets.

Sure, it's true that people move from job to job more often now.

But in our case, many team members have been with us for 5, 8, even 10+ years.

That doesn’t happen by accident.

We’ve always tried to build something stable in an unstable world. No politics. No corporate ladder drama. Just good people solving interesting problems together.

And when life happens, whether it's marriages, kids, or relocations, we adapt.

This year alone, we’ve celebrated milestone birthdays across the team — 30, 40, 50. We’re not just working side by side. We’re walking through life stages together.

That’s why people stay for as long as they do.

Because they know VeryCreatives is a place where you can grow AND feel safe. Where your work matters, but so does your life outside of it.

And when someone does eventually decide to move on?

They don’t just disappear. They stay part of the story. Some drop by for a beer. Others come back to work on new projects.

They leave a mark, and they always will.

The bottom line is this:

In a world that’s constantly shifting, it still matters (more than ever) to build something stable.

Not just a place where you can work. But a place where you can belong.

Helping startup founders & innovation labs validate and scale ideas into exit-ready products @ VeryCreatives | 🔥 Two decades in tech

Almost every time, the founders joining our MVP validation sprints come with a crystal-clear product idea… or so they think:

They have a list of features, a target market, and a deadline.

They want us to say "yes, we love it" and build fast.

Instead, 9 times out of 10, we push back.

We challenge every assumption:
👉 "Why do you need this feature?"
👉 "Has any user actually asked for it?"
👉  "What’s the fastest way to test if people will pay?"

Yes, it causes tension. Nobody likes to have their vision picked apart.

But after 2 days of interviews and rapid prototyping, all the flaws in your core hypothesis start to show up.

Sometimes, this means we cut ~70% of the features people just don’t care about.

And that’s what we’re here for. We help you ditch the noise and focus on what actually will grow your SaaS in the early stages.

It’s not a clean-cut process. It gets messy (and annoying) sometimes.

But our goal is to get you to real validation, a leaner MVP, and a demo users get genuinely excited about.

If you want that and a team that won’t nod along, but challenge you: DM me.

How we go from idea →validated MVP in 6 weeks (step by step):

1. Week 0: Ruthless alignment

We start by killing the founder’s “pet features” and locking in the one problem that makes or breaks the business.

If you can’t explain your core value prop in one blunt sentence, you’re not ready to build.

2. Week 1: Real user interviews (no survey BS)

We get the founder out of their bubble.

Talk to 5-10 target users, live (not with a Google Form).

Ask about their last real pain, not “Would you use this?”

This is where most SaaS ideas die (and that’s a good thing).

3. Week 2: Risk mapping

We list every assumption that could destroy your business model.

Then we grab the riskiest one and design our MVP to smash it fast.

No “nice to haves.” Only “must have” proof.

4. Week 3–4: Design-driven build

We use wireframes to map just the critical user flow – nothing more.

Our dev team builds only what’s needed for a real-life test.

If it feels embarrassingly lean, you’re on the right track.

5. Week 5: First contact

We put your ugly MVP in front of real users.

We measure: 

👉 Will they pay?
👉 Will they switch?
👉 Will they commit?

Crickets or confusion? That’s data, not failure.

6. Week 6: Debrief

If users bite, we double down.

If not, we pivot or kill it.

Every week, clarity beats comfort.

Okay, but how is this sprint different from something you can do on your own?

You get:

👉 A full team (PM, design, dev, QA) running tight Agile sprints.

👉 Weekly reports, no black boxes.

👉 3 months of post-launch support – because the real learning really starts after launch.

More details in the first comment – check it out 👇

The mistake I still see after 15 years building SaaS:

Founders obsess over “v2” features because building feels safer than hearing “no.”

So they polish and perfect, hoping bells and whistles can distract from the core risk:

It happens every quarter.

Another founder sinks months into a slick analytics dashboard.

Custom charts, exports, Slack alerts… you name it.

But when launch day comes, nobody even uploads their first dataset.

The question I’m asking my clients is:

Will anyone care enough to use v0.5?

Every “nice to have” is just a shield against the real test.

And every hour spent hiding behind code is a bet that gets more expensive.

All you need is proof someone will bother to try your ugly, unfinished, brutally honest version.

Start hunting for rejection.

Tag a founder who needs to see this.

taplio

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Dr Emmanuel Ogungbemi

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The Battle

Dr Emmanuel Ogungbemi

Ferenc Fekete

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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);}Dr Emmanuel Ogungbemi's LinkedIn Performance

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