Data Engineer

Hadrian is building autonomous factories to reindustrialize America. By combining AI, advanced software, robotics, and full-stack manufacturing, we help aerospace and defense companies build rockets, satellites, aircraft, ships, and other mission-critical systems up to 10x faster and at significantly lower cost.

Hadrian's Data Analytics team builds and owns the semantic layer that every dashboard, board metric, and AI Analyst answer at Hadrian resolves to. As Hadrian scales from a handful of factories to twenty and beyond, this team makes sure a metric means the same thing in every factory, every dashboard, and every decision — one definition, one number, no matter who's asking. This is the foundation that drives operational intelligence at scale: the layer that turns raw factory data into metrics people can trust and act on. You'll build it in close partnership with Data Platform Engineering, Data Analysts, and the business domains the team serves.

You will architect and own Hadrian's semantic and metric layer — the canonical definitions, calculation logic, ownership, and refresh cadence that keep every team, and the AI Analyst, pulling the same number. Day to day, you'll build certified data marts from cross-domain raw datasets, model them dimensionally to scale from one factory to twenty and beyond, and set the analytical standards the whole team works to — naming, metric definitions, testing, documentation, and CI/CD. You'll run data-quality programs end to end, from profiling and anomaly detection to root-cause analysis, partner with Data Platform Engineering on pipeline architecture, data contracts, and quality SLAs, and mentor analysts on modeling and testing discipline. When a plant manager, a board deck, and the AI Analyst all cite the same yield or on-time-delivery number, it's because you defined it once, in one place.

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  Architect and maintain the certified dataset layer in dbt: models, tests, documentation, and SLAs the whole company trusts.

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  Build well-modeled, context-rich datasets that power self-service analytics, operations research, and LLM-based data apps at company scale.

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  Define metric standards: canonical definitions, calculation logic, ownership, refresh cadence.

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  Implement canonical data models and semantic layer that scale from 1 to 20+ factories.

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  Partner with Data Platform Engineering to harden the unified data platform and set standards.

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  Partner with OR Scientists and Data Scientists on feature-set prep and model-output stores.

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  Evaluate and recommend analytical tooling (BI platforms, notebook environments, metric layers).

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  Define analytical-engineering standards: naming, testing, CI/CD for the dbt project, documentation.

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  Mentor the Data Analysts to drive consistency and governance across datasets.

• 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.

• Cross-functional data marts and pipelines.

• ClickHouse optimization (materialized views, projections, TTL).

• 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.

To conform to U.S. Government export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen or national, lawful permanent resident, protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.

Hadrian uses AI-assisted tools in our recruiting and hiring processes to help our team work more efficiently. This may include tools that help organize and analyze recruiting data, as well as an AI-powered notetaker that can record and transcribe interviews and help coordinate feedback. These tools support our team and are not used to make hiring decisions. All candidate evaluations and hiring decisions are performed by humans. If an interview will be recorded, you will be notified in advance and may opt out at any time with no impact on your candidacy. Candidate data processed through these tools is subject to the same protections described in our Privacy Policy.

Hadrian will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of all applicable laws. We do not ask about criminal history on our application, and we do not consider criminal history before making a conditional offer of employment.

Hadrian does not unlawfully discriminate on the basis of race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, marital status, mental status, physical disability or any other legally protected status. When necessary, the Company also makes reasonable accommodations for disabled candidates and employees, including for candidates or employees who are disabled by pregnancy, childbirth, or related medical conditions.