Senior Data Engineer

Data EngineerShiftbase 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 doArchitect 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 monthsThe 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. What we're looking for You have built warehouse foundations yourself. You designed them rather than maintaining someone else's, with a track record in: Designing layered warehouse architectures (medallion, bronze-silver-gold) and dimensional models (star and snowflake) that teams reuse Advanced SQL as your daily working language Hands-on BigQuery, since our stack is GCP Building a SQL transform layer in Dataform, or strong dbt experience. ELT and ETL with incremental, idempotent loads and real orchestration, scheduling, and dependency management Data quality, testing, freshness monitoring, and lineage built in from the start Python for pipeline code, scripting, automation, and API integration Reviewable, repeatable change through Git and PR-based deployment Nice to have, not required: another modern cloud warehouse such as Snowflake or Redshift; managed ingestion such as Airbyte; preparing data for ML, LLM, and agentic use cases; and familiarity with B2B SaaS metrics and HR or workforce data. English is our working language, and Dutch is not required. This role isn't infrastructure engineering, ML or MLOps, or dashboard analysis. The Platform team owns infrastructure, the Product ML team owns ML, and the business teams own dashboards. It also isn't for someone who has only maintained a warehouse another person designed. You build the foundation properly, stay transparent about data quality and trade-offs, and treat the warehouse as a product for the whole company. You'll report to the Head of Operations and partner closely with the Platform team on the company's data architecture.