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.

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