Data Engineer

Job description

Job responsibilities

• 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.

Job requirements

• 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.