The Python and analytics skills I built at Kactii Academy translated directly into the pipeline engineering, automation, and machine learning work I do today. It gave me a foundation I still rely on every single day.
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Kactii Academy was hands-on from day one. I was writing real Python code, working with actual datasets, and building things that mirrored the kind of work I'd go on to do professionally. That applied approach was exactly what I needed.
Where are you currently working and what is it that you do there?
I'm part of the RWE IT Graduate Programme in Essen, Germany, currently rotating with the Data Excellence team at RWE Generation. My focus is on building production-grade data products and pipelines that support decision-making across the business, all in service of the energy transition.
What does your typical day-to-day look like?
A lot of my time goes into building and maintaining Databricks ETL pipelines — everything from legacy and REST API ingestion to transformation, daily incremental loads, reconciliation, and automated deployments through CI/CD. I also spend a good chunk of my day tracing data lineage across bronze, silver, and gold layers to catch quality issues early, and partnering with cross-functional teams to translate their requirements into scalable data solutions. It moves fast, and I like that — there's always a new problem to solve and a KPI definition to align on.
Can you tell us a bit more about your education and career up to this point? How did you get where you are?
My background is in Computer Science — I did my Bachelor's in engineering, then a Master's in Data Science at FAU Erlangen-Nürnberg. Along the way I worked across a few industries: machine learning at Tata Consultancy Services, BI analytics at Siemens Healthineers, and data strategy at Siemens Energy before landing at RWE. Each role deepened my technical toolkit, but early on I realized I needed stronger, hands-on fundamentals in Python and applied data analytics to really accelerate. That's what led me to Kactii Academy.
What was it that led you to study at Kactii Academy?
I wanted training that was practical, not just theoretical. A lot of programs teach you concepts, but they don't put you in front of real problems the way you'd face them on the job. Kactii Academy was hands-on from day one — I was writing real Python code, working with actual datasets, and building things that mirrored the kind of work I'd go on to do professionally. That applied, project-driven approach was exactly what I needed.
What would you say was the highlight of your learning experience?
Honestly, the moment things "clicked" with machine learning. Going from understanding a model in theory to actually tuning hyperparameters, engineering features, and watching accuracy improve — that was hugely rewarding. The bootcamp structured the learning so each concept built on the last, and the mentorship meant I always had someone to push my thinking when I got stuck.
What was the most challenging part of the learning experience?
Balancing depth with pace. There's so much ground to cover between core Python and applied analytics, and I wanted to master all of it. Learning to focus on the fundamentals first — really solidifying them before moving on — was a discipline the bootcamp instilled in me, and it's paid off in every role since.
How has your Kactii Academy experience impacted your career?
It gave me a foundation I still draw on daily. The Python and analytics skills I built there directly translated into the pipeline engineering, automation, and ML work I've done at Siemens and now at RWE. Being able to write clean, efficient code and reason through a data problem end-to-end is what let me take on more ambitious projects — and it's a big part of how I ended up on RWE's Data Excellence team.
What advice would you give to other professionals thinking about taking a Kactii Academy bootcamp?
Do it — but come ready to put in the work. The value is in how hands-on it is, so the more you engage with the projects and lean on the mentorship, the more you'll get out of it. If you're serious about breaking into data or leveling up your existing skills, this is one of the fastest ways to build a real, applicable foundation.
