Data Engineer

Join our team as a Data Engineer, where you will architect and maintain the certified dataset layer in dbt, build well-modeled datasets for self-service analytics, and define metric standards. You will partner with Data Platform Engineering, Operations Research Scientists, and Data Scientists, evaluate analytical tooling, and define analytical-engineering standards. Additionally, you will mentor Data Analysts to drive consistency and governance across datasets. Enjoy benefits such as a relocation stipend, 100% coverage of platinum medical, dental, vision, and life insurance plans, a 401k, and a flexible vacation policy.

• Architect and maintain the certified dataset layer in dbt, including models, tests, documentation, and SLAs.
• Build well-modeled, context-rich datasets that power self-service analytics, operations research, and LLM-based data apps.
• Define metric standards, implement canonical data models and semantic layer that scale from 1 to 20+ factories.
• Production ownership of data models (years scale with level; see Level & Justification)
• End-to-end ownership, with a quality bar that doesn't stall progress
• Python for reusable pipeline and data-app utilities
• Familiar with lake and warehouse internals (columnar stores, Iceberg catalog, partitioning, materializations)
• Builds semantic layers with dbt, Snowflake, or Databricks
• Strong data-modeling foundation (normalization, denormalization, star/snowflake schemas)
• Ships production data pipelines with Spark, dbt, and Dagster or equivalents
• Expert SQL (window functions, CTEs) with a real grasp of query performance and cost
• ClickHouse optimization (materialized views, projections, TTL)
• Cross-functional data marts and pipelines
• Manufacturing statistics: SPC, control charts, process capability
• Data mesh and data-product concepts
• Orchestration depth (Dagster, Airflow)
• Scaling analytics across multiple sites or business units
• Background in Operations Research, industrial engineering, or quantitative finance