Plot twists
& data.

I graduated with a First Class degree in Computer Science, but I never wanted a career in tech. Ironic, right? I know.

I wanted to be a fashion designer. In fact, I started my fashion business while I was still in university, got contracts to sew uniforms for multiple schools, and was happily living my dream.

Then disaster struck.

I had to intern at a reputable tech company and actually write code.

What was supposed to be a six-month internship became twelve months, courtesy of COVID. Somewhere within that year of learning and collaborating with other tech geniuses, I fell in love with technology.

Who would have thought?

In 2021, during my final year at university, technology stopped being something I learned in school and became something I could use to solve real problems.

I joined the Click-On Kaduna Data Science Fellowship and worked with SDG health-indicator data for the Kaduna State Ministry of Health through a program partnered with the Bill & Melinda Gates Foundation.

I also helped pioneer a collaboration with the founder of Open Data Kit (ODK) to improve data collection in remote parts of Nigeria, and used Power BI and Azure to improve how Kaduna State’s health data was analysed and used.

That work earned commendation from the UN Deputy Secretary-General and the Governor of Kaduna State for visualising large datasets covering hospital infrastructure, personnel, services and equipment.

I joined DataedX Group in 2022, working at the intersection of data, AI, public policy and ethics.

One of the major projects I worked on was the Atlanta Interdisciplinary AI Network (AIAI), whose research received $1.3 million in funding from the Mellon Foundation, supporting interdisciplinary work across Emory University, Clark Atlanta University and Georgia Tech.

I led data analysis and visualisation around AI governance in Georgia, investigating how legislation was keeping pace with AI development. I also worked on AIAI’s Public Interest AI research, using Python and SQL to investigate Atlanta’s data practices, including a car-insurance project that uncovered a 26% racial disparity in insurance rates around Atlanta’s I-20 corridor.

That work eventually led me into research on cybersecurity, digital sovereignty and African data infrastructure, and to presenting at Deep Learning Indaba. I later wrote For Africa, By Africa, exploring Africans building AI systems for African problems and what genuine technological self-determination could look like.

Lately, my curiosity has gone even further down the stack into the physical infrastructure behind AI: data centres, water, energy, incentives and who ultimately absorbs the cost of all this infrastructure.

After analysing hundreds of research papers, datasets and legislative bills, I became curious about the systems behind all that data.

Where did it come from? How did it get there? What happens before a nice, clean dataset lands in front of an analyst or researcher?

So I went deeper.

Today, I work as a Data Platform Engineer, building data systems and the infrastructure they run on for analytical and AI workloads.

My engineering work has included automated pipelines, serverless data lakes, cloud networking, Infrastructure as Code and end-to-end analytical data platforms, with hands-on experience across Python, SQL, AWS, Snowflake, Terraform, Docker, Kubernetes, Airflow, dbt, Airbyte and Kafka. My engineering practices include Infrastructure as Code, CI/CD, orchestration, dimensional modelling, IAM, RBAC and least-privilege access.

I’ve architected and built an end-to-end cloud data platform that moved raw data through automated Bronze → Silver → Gold layers, with orchestration, dimensional modelling, Infrastructure as Code, CI/CD and least-privilege access.

Another automated a Finance workflow that previously required roughly two hours of manual work every day and helped prevent a €50,000 OpEx miscalculation.

A lot of my professional engineering work can’t live publicly on this website, which is probably the very normal curse of doing infrastructure work for actual organisations. 😂

But doing this work has fundamentally changed how I think about AI. I’ve seen organisations struggle to get the best out of their AI initiatives, and increasingly, I believe a big part of the solution is sitting right underneath them: reliable, governed, observable and secure data platforms.

That intersection between data platforms, infrastructure and production AI is where I’m increasingly interested in building.

A lot of my success can be traced back to people who saw me and decided to make my progress part of their life’s mission. So I’ve decided to do the same for others.

I’ve taught data analysis, Python and Excel, including working with the Nigerian government’s 3MTT program to support more than 60 learners.

More recently, I created over three hours of instructional material for a free Generative AI for Data Analysis course through Open Visualization Academy, supported by the University of Miami and Knight Foundation.

And yes, I still make educational videos on the internet explaining data, cloud and AI concepts in normal English… and somehow about 8,000 people find my content helpful. 😂

I’m a 2× Tableau Ambassador and co-lead the AI + Tableau User Group, a community of 850+ members across 12 countries.

We’ve brought the people building these AI products into conversations with the people actually using them, including hosting Tableau EVP & GM Mark Recher for The Future of Tableau and AI. I’ve also represented the community at Tableau’s DataFam LIVE, alongside leaders from Tableau/Salesforce, JPMorgan Chase and the wider AI ecosystem.

I also manage programs for a 7,000+ member Data Engineering Community, where I’ve launched the 30 Days of Linux Challenge, worked on community operations and learning programs, organised an industry session featuring a Senior Developer Advocate from Confluent, and am currently working on our second physical meetup.

If you combine data analysis + research + data engineering, sprinkle in AI, infrastructure, community building and an unreasonable amount of curiosity, you pretty much get me.

I’m a Data Platform Engineer who builds data systems and the infrastructure they run on for analytical and AI workloads.

I’m building data platforms today, increasingly exploring the infrastructure required for production AI, and continuing to write, teach and speak about what I’m learning along the way.

A moving woman will definitely meet her luck! Or how do the kids say it these days? Lol.

And when I’m not battling it out with data, you’ll probably find me yapping about my faith, books, music, or psychology.