Data engineer for recommendation systems

A major European streaming group is rolling out a new recommendation model across three of its platforms. The model is built and tested. What’s needed now is the measurement framework that proves it actually moves engagement. That’s this role. Two senior data engineers, joining an existing team, working to a tight delivery window.

• The A/B testing framework, operational across three separate platform environments
• Audience segmentation and activation — getting the right viewers into the right tests
• ETL pipelines in Databricks (SQL and Python) feeding the experimentation layer
• Optimizely SDK and webhook integration for test setup and measurement
• Real-time engagement metric collection, dashboards and automated alerting on test health
• Fine-grained permissions with OpenFGA across the analytics architecture

• Strong Databricks (SQL and Python) and production ETL experience
• Hands-on experimentation work — A/B test infrastructure, not just reading the results
• Optimizely or comparable experimentation platforms
• Analytics architecture and real-time metrics at consumer scale
• Senior enough to be productive in week one. The window is short and the goal is shipped measurement, not onboarding
• AI literacy at L3 or above
• L3 means you run full workflows with skills, sub-agents and MCP servers, and you build reusable AI artifacts that other engineers actually adopt — not just agent-mode coding on your own machine.
• Be ready to show concrete evidence: the skills, sub-agents, MCP servers or agentic workflows you’ve built and used on real projects.
• Будет плюсом: Candidates who orchestrate several agents in parallel are especially welcome

• Условий в вакансии нет