Every day at Moniepoint, we process over 750 million transactions monthly, leveraging data to provide financial happiness to users across our business and personal banking products.

In this lightning round, Habib, our Data Engineering Lead, breaks down why he loves working with data and how he got into data engineering.

What led you to Moniepoint?

I got into tech while I was still in university, studying mechanical engineering. I used to work on drones, so I learned to code because I wanted to build drones that could fly themselves. This main motivation to learn how to build drones, over time, evolved into something I enjoyed.

Tosin, Moniepoint’s CEO, came to deliver a lecture at OAU around the time I was getting immersed in programming. I was not at the lecture, but people told me about it and how good it was. This registered Moniepoint (then TeamApt) in my mind, and I was fascinated. 

A few days later, someone shared a vacancy at Moniepoint. I applied and went through the interview, and that's how I got into Moniepoint four years ago. Towards the end of 2019, I returned to school for my Masters degree and rejoined the company afterwards. 

Can you think of anything you've enjoyed working on in the last year?

The most interesting thing I've worked on recently is this data pipeline for our business loans product. I worked on a data pipeline that gathers our data from all our data sources, harmonises the data, and presents it in a way the product can understand.

That's the largest data set I've worked with, so it was challenging getting it to work. If you have a very large data set it can take days to process if you don't do things the right way. So the challenge was to make it run as fast as possible, and achieve something workable and usable by the product.

If you were to describe your role as a type of food, what would it be?

My role as a data engineer is like ponmo (ponmo refers to cow skin, a favourite beef part enjoyed as a side dish, snack and condiment in southwestern Nigeria). Yes, I'll compare it to ponmo, and the reason I'm describing it that way is because sometimes it's soft work, and sometimes it's so tough, it almost blows your teeth dry. 

What’s one weird challenge you’ve dealt with?

One of the main challenges I have at work is talking to or maybe reaching out to people. You see, I’m incredibly shy, and if I absolutely have to reach out to someone, I might think about it for an hour before making the move. Sometimes, I will just talk to a person I already have a rapport with to help me talk to the person. 

But with time, I've been getting better at reaching out to people.

What’s your favourite M, in the 4Ms Framework?

Out of the four M's the one that resonates with me the most is mastery. And that's because whenever I take something or start working on something new I'm not always satisfied with just being ordinary. I always want to take it to the next level.

At least be the at least best or the closest to being the best at doing that thing. So it's mastery. If I wasn't a data engineer I'd probably be a university professor. I might still become one in the future. 

What do you love most about working with Data?

What I love about working with data is that data doesn't lie. It's pretty much the way a scientist makes hypotheses.

You have a question, conduct research, establish your hypothesis, then run an experiment to verify whether your hypothesis is true, and analyse the results. 

Working with data is similar to that. You can hypothesise that on Fridays, you're likely to make more money because of a couple of conditions or events. Then you work with and check your data, you run your analysis and find out, “Oh okay, it's not actually Friday”, maybe it's Mondays you're supposed to make more money.

So, just like the scientific process, you make a hypothesis, and you verify your hypothesis.

If you found this fascinating, you should check out the video version, and stop by our careers page to see our open roles.