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.

Mario Hernandez

Turning LinkedIn into a Fundraising Engine for Nonprofits | Keynote Speaker | Investor | Husband & Father | 2 Exits |

I spent years building and scaling companies, raising investment, and developing partnerships across different industries. What I learned is that growth always comes down to one thing: relationships.

When I started working with nonprofits, I noticed something that changed my direction. Many organizations were approaching corporate partnerships as sponsorship requests rather than co-created impact. The conversation was transactional, so the funding was short term.

So now I help nonprofits secure meaningful corporate partnerships and long term funding through a relationship first strategy. I also train nonprofit consultants who want to deliver this framework with their clients.

My work at Impact Circle focuses on:
• Building partnership pipelines that are based on alignment, not pitching
• Training nonprofit teams to lead high trust conversations with corporate partners
• Helping consultants grow their visibility and revenue using a value first approach

I still build companies that create impact. I am developing Givly, an AI powered platform designed to help nonprofits increase grant success by giving teams a research and writing co pilot that understands their goals.

A few parts of my journey:
• Built and exited multiple companies
• Raised millions in investment
• Recognized by JA Worldwide for creating meaningful impact
• Reduced over one million single use plastic bottles through sustainable water systems
• International keynote speaker on social impact, partnerships, and purpose driven growth

If you believe relationships are the real engine of funding and partnership success, we are aligned.

Send me a message with the word Impact, and I will share resources we use to help teams grow sustainably.

Let’s build something meaningful.


Co-Founder & CEO

- Rethink Philanthropy. Join the first social media network dedicated to philanthropy, connecting individuals, businesses and non profits with the mission of maximizing impact for our planet. 

- Knobl's innovative campaign event manager turns your favorite everyday activities into tools for recollecting donations. Play Fortnite, listen to music on Spotify, drive your Tesla, or workout with Apple Health and turn all of these activities into philanthropic campaigns. 

- Sign up to start making a difference at http://knobl.one

- Reached $3.5M valuation in 1 year, currently fundraising in SEED round.

Here’s $7,500 worth of LinkedIn training for nonprofits.
For free.
No catch, no upsell:

Just 8 courses that show you exactly how to attract donors, sponsors, and partners on LinkedIn. 

The same system our clients use to raise thousands without cold calling.

All I ask? Tag a nonprofit leader or fundraiser in the comments so they can grow too.

𝗖𝗼𝘂𝗿𝘀𝗲 𝟭: How to Pitch Your Nonprofit

👉 https://lnkd.in/gDJvE8Dg
• Craft a pitch that moves hearts and opens checkbooks
• Use storytelling to make your mission unforgettable
• Borrow my exact pitch templates used by top fundraisers

𝗖𝗼𝘂𝗿𝘀𝗲 𝟮: How to Use LinkedIn to Attract Corporate Donors

👉 https://lnkd.in/gdUKhsXT
• Build a corporate partnership pipeline from scratch
• Turn connections into recurring sponsors
• Learn the message frameworks that win attention and funding

𝗖𝗼𝘂𝗿𝘀𝗲 𝟯: Optimize Your Profile to Become Fundable

👉 https://lnkd.in/gjU8NPKF
• Transform your profile into a magnet for donors and partners
• Use credibility cues that trigger trust
• Get found by decision-makers looking to fund causes like yours

𝗖𝗼𝘂𝗿𝘀𝗲 𝟰: 16-Minute Breakdown of a Nonprofit’s Personal Brand

👉 https://lnkd.in/gcW7rk_h
• Watch a live teardown of a nonprofit leader’s profile
• Learn the quick wins that boost visibility overnight
• Discover how branding drives donations

𝗖𝗼𝘂𝗿𝘀𝗲 𝟱: How to Use LinkedIn’s Search Function

👉 https://lnkd.in/gwUK-bem
• Find hidden corporate donors and aligned partners
• Use advanced filters to locate decision-makers
• Build a warm lead list in minutes

𝗖𝗼𝘂𝗿𝘀𝗲 𝟲: From Zero to 100 Direct Messages

👉 https://lnkd.in/gKepVRau
• Go from invisible to in-demand
• Use my exact outreach scripts that drive replies
• Turn conversations into funding opportunities

𝗖𝗼𝘂𝗿𝘀𝗲 𝟳: Brainstorm Nonprofit Content with AI

👉 https://lnkd.in/gK5297Jw
• Never run out of post ideas again
• Use AI prompts built for nonprofit storytelling
• Spark engagement from donors and sponsors daily

𝗖𝗼𝘂𝗿𝘀𝗲 𝟴: Your Own Nonprofit LinkedIn Content GPT

👉 https://lnkd.in/gpwPJivT
• Generate scroll-stopping posts instantly
• Customize your voice and mission tone
• Create consistent, high-impact content in minutes

Total value: $7,500+
Your investment: $0

Why am I giving this away?

Because too many nonprofits are doing incredible work… but staying invisible.

These are full trainings and the same strategies that helped leaders raise $30,000+ in weeks.

When your mission wins, the world wins.

P.S. Yes, this is real. Yes, it’s free. No, there’s no catch.

I work with nonprofits in high-touch programs, this is how I give back to the sector that gives so much.

With purpose and impact, Mario

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Many nonprofits lose 6-figure deals because they say 1 sentence the wrong way (here are 5 shifts that fix it fast): 

You did everything right. Built trust. Earned the meeting.
And now… you’re at the ask.

This is where most nonprofits lose the deal 
Not because of the budget.
But because of a sentence.

The wrong words at the wrong time can quietly kill what could’ve been a 6-figure, multi-year collaboration.

Here are 5 sentence-level shifts that make the difference:

1. “We’d be honored by your support.”
❌ Sounds like charity.
✅ Say instead: “We built this with leaders like you in mind.”


2. “We’re hoping you’ll consider a gift.”
❌ Hope = hesitation.
✅ Say: “We’re aligning with partners driving systemic change.”


3. “This donation will help us continue our work.”
❌ Vague. No stakes.
✅ Try: “Here’s what your partnership will create in 12 months and who it’ll impact.”


4. “Can we count on your generosity?”
❌ Passive.
✅ Use: “We’d like to co-design a high-impact initiative you’re proud to champion.”


5. “Any amount helps.”
❌ Minimizes the value of the opportunity.
✅ Flip it: “We only partner when the impact matches the investment.”


The ask isn’t the problem.
The framing is.

You don’t close major funding by sounding grateful.

You close it by sounding aligned.

If you’re ready to start landing major, multi-year partnerships, drop “partnerships” in the comments and connect with me.

I’ll send you a free resource our clients are using to build a pipeline of ideal partners on LinkedIn. 

With purpose and impact, Mario

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The Battle

Dr Emmanuel Ogungbemi

Mario Hernandez

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Performance ScoreDr Emmanuel Ogungbemi's LinkedIn Performance

Performance ScoreMario Hernandez's LinkedIn Performance

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taplio

The Battle

Dr Emmanuel Ogungbemi

Mario Hernandez

Followers

Posts

Engagements

Likes

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

Performance ScoreMario Hernandez's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreDr Emmanuel Ogungbemi's LinkedIn Performance