AI Data Engineer
Summary
12-month contract AI Data Engineer for a product-led fintech trading platform in the Netherlands. Day to day: build a centralised data warehouse and Python pipelines, own ML models for churn/segmentation/personalisation, and prototype LLM-powered tools using SQL, Python, dbt/AWS Glue and AWS SageMaker.
AI Data Engineer | Fintech | 12-Month Contract | Netherlands
The details
Based in the Netherlands
Right to work in the EU required
Turn trading data into experiences customers actually want to come back to.
Our client is a product-led fintech platform serving a large, global base of active traders. They're investing heavily in data and AI to understand their customers better, keep them engaged for longer and make trading feel more personal. They now need a contract AI Data Engineer to help make that happen.
This role blends data engineering, machine learning and GenAI with a strong commercial focus. You'll work closely with marketing and product teams, so the ability to connect technical work to business outcomes matters just as much as the code.
Where you'll spend your time
Building the foundations: Creating a centralised data warehouse and well-structured datasets that multiple teams can rely on for retention and lifetime value reporting
Predicting behaviour: Developing and owning ML models for churn prediction, customer segmentation and personalisation, then helping marketing put them to work across channels
Experimenting with GenAI: Building LLM-powered tools that simplify trading and tailor the experience to each customer's preferences
Connecting the dots: Writing Python pipelines and automations that pull together internal databases, marketing platforms and third-party APIs
Spotting opportunities: Finding new ways to use data and AI to lift engagement or automate manual work across the business
What you'll bring
- Advanced SQL and confidence working with large, messy datasets covering customer journeys, product usage and marketing performance
- Strong Python for building pipelines, automations and API integrations
- End-to-end ML delivery in Python, from building a predictive or segmentation model to getting it running in production
- A track record of uncovering customer insights and turning them into strategies that improve engagement and LTV
- Hands-on experience building LLM applications, such as summarising large volumes of data, plus a good feel for current GenAI use cases
- Exposure to ETL tools like dbt or AWS Glue, and cloud platforms, ideally AWS and SageMaker
- The ability to explain complex findings clearly to non-technical audiences
Experience in trading, fintech or another high-volume consumer platform, and a background supporting marketing or CRM teams with data.
