Frederic Marechal

Can you tell us about your role at BI:PROCSI? What will you be working on as a Senior Data Engineer?

I am thrilled to announce that I am joining BI:PROCSI as a Senior Data Engineer.

The data landscape is evolving faster than ever — and versatility is no longer optional.

This role goes well beyond conventional data engineering. Alongside architecting robust storage, processing, and modelling pipelines to industry ETL/ELT standards, I will be applying machine learning and data science to surface actionable insights — and developing Agentic AI solutions that push the boundaries of what modern data platforms can deliver.

Working with cutting-edge products and world-class clients, this is precisely the kind of opportunity where engineering rigour meets applied intelligence.

Excited for what lies ahead.

Can you share your journey and experience in software development that led you to become a Data Engineer?

My journey into data engineering did not begin with a job title — it began with a problem to solve.

Over a decade in Investment Banking, I was building data warehouse architectures, engineering ETL pipelines for the risk and trading reporting systems. Nobody called it data engineering back then. But that is exactly what it was.

That foundation sparked a deliberate pivot into data science/machine learning — across Commercial Banking, Telehealth, Digital Marketing and Adtech. Each role added depth, and highlighted the need for consistent, accurate and robust data delivery to achieve the business goals.

Ultimately, the two disciplines converged: in my previous job I led a team of data engineers and introduced AI-assisted development.

What were the key factors that made you choose to join BI:PROCSI?

It is about the problems you will solve, the technologies you will master, and the clients who will challenge you to be better.

BI:PROCSI ticks every one of those boxes.

World-class clients across fintech, gaming, e-commerce, media, etc. A genuinely modern stack — Snowflake, Databricks, dbt, Fivetran, AWS, GCP — with no technology bias. AI and ML treated as a first-class discipline, not a buzzword. And end-to-end ownership from strategy to production, because real engineers do not hand things over at the pipeline door.

For a data engineer who refuses to be put in a box, this is exactly the kind of environment where craft meets ambition.

Finally, when you’re not designing data pipelines or solving complex data challenges, what do you enjoy doing in your free time?

Outside of work? I operate as a husband and dad — which mostly means enforcing homework delivery pipelines to spec and on schedule (occupational hazard, clearly) And when I am not doing that, I am at the gym — because staying young is a long-running job with no deprecation date.