Senior Data Engineer

Data Engineer Shiftbase is building the self-driving assistant for shift-based businesses across Europe. Our customers range from independent restaurants in Amsterdam to multi-location retailers in München, and they spend hours every week on scheduling, time tracking, payroll prep, and HR admin. We want to take that work off their plate and eventually do it for them. Underneath that product vision sits a data challenge. Insight, automation, and AI all depend on data people can trust, and the foundation for it doesn't exist yet. You will build the foundation that fixes this. What you'll do • Architect the warehouse. Design and own our BigQuery and Dataform warehouse from the ground up. Build a layered, well-modeled foundation, medallion staging through marts, not only the models that sit on top. Ask every day whether the whole company can trust and reuse it. • Make the data flow reliably. Build ELT and ETL with incremental, idempotent loads, orchestration, and retries, so pipelines run on time without anyone watching them. Ship monitored pipelines that hold up in production. • Take the transform layer. Own the Dataform model-building and transform layer, taking full responsibility for the transformation layer and freeing up capacity for semantic modeling and business definitions. This unblocks the whole team. • Build trust into the data. Make validation, freshness monitoring, alerting, and lineage the default. Diagnose failures at the root cause. leadscorev2 is the outcome this role exists to prevent. • Serve the whole company. Feed clean, governed datasets to Metabase, HubSpot, and the ML and AI work the product ML team owns. Push modeled data back into the tools business teams act on, including HubSpot and Customer.io . • Own the Platform handshake. Run the day-to-day collaboration with the Platform team on our GCP foundation, a bridge that only gets part-time attention today. • Treat the warehouse like a product. Keep documentation, lineage, and discoverability current so departments trust the data assets, and handle sensitive HR and workforce data to GDPR standards. What success looks like in your first 3–6 months • The warehouse has a real foundation. The first vertical slice, our accounts model, ships with tests and monitoring. • You fully own the Dataform transform layer, creating capacity for semantic modeling and business-definition work • Pipelines run against a defined freshness SLA, and you catch breaks before stakeholders do. • Documentation and lineage cover what you own, and datasets that were stuck now flow to the teams that need them. •