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Hadrian

Data Engineer

Los Angeles, CAFrom $150kfulltimemidAdded 3 days ago

About this role

Hadrian seeks a Data Engineer to architect and maintain the semantic and metric layer that powers analytics across its autonomous factories. You'll build canonical data models in dbt, define company-wide metric standards, and ensure operational intelligence scales reliably from a handful to 20+ factories.

What you'll do

  • Architect and maintain certified dataset layer in dbt with models, tests, documentation, and SLAs
  • Build dimensional data marts and semantic layers that support self-service analytics and AI applications
  • Define and enforce metric standards including canonical definitions, calculation logic, and ownership
  • Implement scalable canonical data models as Hadrian expands from 1 to 20+ factories
  • Partner with Data Platform Engineering on pipeline architecture, data contracts, and quality SLAs
  • Mentor Data Analysts on modeling discipline, testing standards, and analytical engineering best practices

What they're looking for

  • Production data modeling and dimensional design (star/snowflake schemas)
  • Expert SQL with window functions, CTEs, and query optimization
  • dbt for building and maintaining semantic layers
  • Data pipeline orchestration (Dagster, Airflow, or equivalent)
  • Spark for distributed data processing
  • Python for pipeline utilities and data applications
  • Data warehouse/lake internals (columnar stores, Iceberg, partitioning)
  • Data quality and testing frameworks
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Hadrian

Hadrian builds aerospace and defense manufacturing systems, offering enterprise software platforms, advanced tooling design, and highly automated production capabilities for the sector. The company is hiring full stack engineers, manufacturing and tooling specialists, infrastructure and identity management experts, and workforce systems architects to support its rapid scaling.

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Likely interview questions

  • Walk us through a time you designed a dimensional model that scaled across multiple business domains or geographic regions—what challenges did you face and how did you handle metric consistency?
  • Describe your approach to defining and enforcing metric standards across a large organization. How do you handle conflicting definitions from different teams?
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