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Salary: USD 150,000 - 230,000 / annual
Hadrian is building autonomous factories to reindustrialize America, combining AI, advanced software, robotics, and full-stack manufacturing to help aerospace and defense companies build rockets, satellites, aircraft, and mission-critical systems up to 10x faster and at lower cost. Following a $1.37B Series D at $7.87B valuation, the company is rapidly expanding its manufacturing footprint and Factory-as-a-Service platform.
You will architect and own Hadrian's semantic and metric layer—the canonical definitions, calculation logic, and refresh cadence that ensure every team and AI system pulls the same trusted number across all factories. This role is foundational to operational intelligence at scale, turning raw factory data into metrics people can trust and act on.
Day-to-day responsibilities include: architecting certified data marts from cross-domain raw datasets using dbt; modeling them dimensionally to scale from one factory to twenty and beyond; setting analytical standards (naming, metric definitions, testing, documentation, CI/CD); running data-quality programs end-to-end from profiling and anomaly detection to root-cause analysis; partnering with Data Platform Engineering on pipeline architecture, data contracts, and quality SLAs; and mentoring analysts on modeling and testing discipline.
You'll build well-modeled, context-rich datasets powering self-service analytics, operations research, and LLM-based data apps. You'll define metric standards with canonical definitions and ownership, implement canonical data models scaling from 1 to 20+ factories, evaluate analytical tooling, and ensure consistency and governance across datasets.
Required: production ownership of data models at scale; expert SQL (window functions, CTEs) with query performance and cost optimization; shipping production pipelines with Spark, dbt, and Dagster or equivalents; strong data-modeling foundation (normalization, denormalization, star/snowflake schemas); familiarity with lake and warehouse internals (columnar stores, Iceberg catalog, partitioning); semantic layer experience with dbt, Snowflake, or Databricks; Python for utilities; end-to-end ownership with quality discipline.
What sets you apart: cross-functional data marts and pipelines; ClickHouse optimization; manufacturing statistics (SPC, control charts, process capability); data mesh and data-product concepts; orchestration depth; scaling analytics across multiple sites; background in Operations Research, industrial engineering, or quantitative finance.
