Where the Data Engineer builds pipelines, you own the platform. Rocket’s data layer has to scale from a handful of European hotel groups to enterprise chains across two continents — multi-tenant, reliable, cost-efficient, and fast enough to serve both real-time product features and ML workloads.
You will set the architecture, the standards, and the roadmap for our Databricks lakehouse, and raise the bar for everyone working on data at MICE DESK.
- Data platform architecture. Own the design of our lakehouse (Medallion architecture on Databricks): tenancy model, storage layout, orchestration, streaming vs. batch strategy, cost management, and the evolution path as customer volume grows 10x.
- Scalable multi-tenant ingestion. Design the ingestion framework that makes onboarding a new hotel group a configuration task, not an engineering project — across wildly heterogeneous PMS/CRM sources and both EU and US data residency requirements.
- ML and product data serving. Build the interfaces between the data platform and its consumers: feature pipelines for our ML models, low-latency serving for in-product analytics, and the contracts that keep both stable.
- Standards and mentorship. Define engineering standards for the data team — testing, CI/CD for pipelines, documentation, data contracts — and mentor data engineers toward them.
What success looks like:
- 3 months: You’ve audited the current platform, set the target architecture, and shipped the first structural improvement.
- 12 months: The platform onboards new enterprise customers in days, serves ML and product reliably, and the data team operates at a visibly higher standard.