Great technology is built by curious minds. Here, we prioritise people first, fuelling a culture where asking ‘why’ is just as important as knowing ‘how.’ Dive into our employer blog to find out what makes Monteers tick – both in and outside of the office.
Next up: Gaëlle Greiveldinger, Senior Data Analytics Engineer, based in Copenhagen. Whether she’s clocking a long run across the city on a sunny weekend, jumping into a freezing canal for the adrenaline shot, or quietly rebuilding the data foundation the rest of Monta runs on, Gaëlle brings a calm, methodical energy to everything she does. Her job, in a sentence: make sure the rest of us can answer our own data questions – and trust the answers we get.
Take us behind the scenes. What challenge are you working on right now that really excites you?
The big one we’ve been working on in the team is self-serve data at Monta. The idea is that everyone at Monta should be able to access the data they need independently, utilising our AI tools. This empowers individuals and means the data team isn’t relied upon for simple requests – “how many active charge points do we have in Germany?”
We’ve done a lot of work to make that easy: setting up the framework, building intuitive ways for people to find what they’re looking for, and adding documentation so they actually understand what they’re seeing. The goal is that the data team gets out of the way, and people can go fishing for what they need themselves.
The technical side is one challenge – having the right foundation in place. But the bigger one is adoption. We need to change the mindset, so that instead of pinging the data team people stop and say, “wait, I can figure this out myself.” Both pieces have to come together: if the tools aren’t easy enough, people won’t try; and even when they are, we have to actively nudge each other – “did you try it yourself? Here’s how”, instead of just answering every question.
It also connects to Monta AI. For self-serve data to work, and for tools like ‘Monta Ask’ to give good answers, the foundation has to be solid: real data, clean models, good documentation. AI inherits the quality of what’s underneath it. So it’s all the same project, really.
When do you feel most supported by your team?
Honestly, every day. We have a very open culture where we are sharing the progress we are making and the challenges we are facing to get help from each other. As we work with code, we are also doing peer reviews. . That means we’re constantly interacting – giving feedback, proposing changes, challenging an approach, or just leaving a thumbs up and “oh, very nice, that’s an elegant way of doing it.”
It’s a small thing, but that steady back-and-forth is what makes me feel part of a team rather than someone working on my own. It’s a close-knit group, and you really get to know how each person thinks.
What fuels you when the laptop closes?
I run and do CrossFit, several times a week. I like to train in the morning – it sets me up with good energy for the day. On weekends, when there’s more time, I love going for a long run across the city with a friend or my fiancé, especially if the weather plays along.
Since moving to Copenhagen, I’ve also gotten into winter bathing. That one’s more about the reset – and the shot of adrenaline. I don’t usually combine it with running, though. The logistics of getting cold, then warm, then cold again is a bit much, and winter bathing for me is the cosy version: sauna, hot drink, layers of clothes. Running is a different mode entirely.
The other thing I love is cafe culture. Stopping for a coffee in the middle of the day always feels a little bit like being a tourist in your own city.
What are you most excited to build with your team over the next six months?
There’s good momentum in the team right now around improving both our data foundation and our ways of working with AI – growing while optimising, automating and making our lives easier. There’s still plenty to do, and I’m excited to keep pushing on it together with the team.
It feels especially important now because everything is connected. For self-serve data to work, we need solid infrastructure, good documentation, and reliable data quality. For Monta AI to give good answers, same thing – the foundation has to be there. So the work we’re doing in the engine room ends up enabling a lot of the more visible things across Monta.
For the next stretch, it’s really about that: keep growing, keep automating, and make sure operations run smoothly so the rest of the company can build on top of what we’ve got.