Data Engineer, Hadrian

• Production ownership of data models (years scale with level; see Level & Justification)
• Expert SQL (window functions, CTEs) with a real grasp of query performance and cost
• Ships production data pipelines with Spark, dbt, and Dagster or equivalents
• Strong data-modeling foundation (normalization, denormalization, star/snowflake schemas)
• Familiar with lake and warehouse internals (columnar stores, Iceberg catalog, partitioning, materializations)
• Builds semantic layers with dbt, Snowflake, or Databricks
• Python for reusable pipeline and data-app utilities
• End-to-end ownership, with a quality bar that doesn't stall progress

Desirable

• Architect and maintain the certified dataset layer in dbt: models, tests, documentation, and SLAs the whole company trusts
• Build well-modeled, context-rich datasets that power self-service analytics, operations research, and LLM-based data apps at company scale
• Define metric standards: canonical definitions, calculation logic, ownership, refresh cadence
• Implement canonical data models and semantic layer that scale from 1 to 20+ factories
• Partner with Data Platform Engineering to harden the unified data platform and set standards
• Partner with OR Scientists and Data Scientists on feature-set prep and model-output stores
• Evaluate and recommend analytical tooling (BI platforms, notebook environments, metric layers)
• Define analytical-engineering standards: naming, testing, CI/CD for the dbt project, documentation
• Mentor the Data Analysts to drive consistency and governance across datasets