Data Engineer

About the role

Hadrian is constructing autonomous factories to reindustrialize America, and the Data Engineer in this role owns the foundational systems that make this transformation possible. You will architect and maintain the semantic and metric layer that serves as the single source of truth for every dashboard, board report, and AI Analyst answer across the entire manufacturing network. This position requires you to build certified data marts from cross-domain raw datasets, ensuring that a metric means the same thing and produces the same number in every factory, regardless of who is querying it. You will work in close partnership with Data Platform Engineering, Data Analysts, and business domain leaders to define analytical standards that scale from a single factory to twenty and beyond. The role demands expertise in setting naming conventions, metric definitions, testing strategies, documentation, and CI/CD practices for the analytics lifecycle. You are responsible for running data-quality programs end to end, including profiling, anomaly detection, and root-cause analysis in collaboration with data platform and domain teams. When a plant manager, a board deck, and the AI Analyst all cite the same yield or on-time delivery number, it is because you defined it once, in one place, and ensured its integrity at scale.

Key facts

What you'll do

• Architect and maintain the certified dataset layer in dbt, including models, tests, documentation, and SLAs that the entire company trusts.
• Build well-modeled, context-rich datasets that power self-service analytics, operations research, and LLM-based data applications at company scale.
• Define metric standards by establishing canonical definitions, calculation logic, ownership, and refresh cadence for all analytical outputs.
• Implement canonical data models and a semantic layer that scale efficiently from one factory to twenty-plus factories without loss of consistency.
• Partner with Data Platform Engineering to harden the unified data platform and establish standards around data contracts, pipeline architecture, and quality SLAs.
• Partner with OR Scientists and Data Scientists on feature-set preparation and model-output stores to ensure analytical readiness for advanced use cases.
• Evaluate and recommend analytical tooling, including BI platforms, notebook environments, and metric layer technologies, to support growth.
• Define analytical-engineering standards covering naming conventions, testing practices, CI/CD for the dbt project, and comprehensive documentation.
• Mentor Data Analysts to drive consistency and governance across datasets, fostering a culture of data quality and reliability.
• Run data-quality programs end to end, from profiling and anomaly detection through root-cause analysis and remediation strategies.
• Ensure that critical metrics such as yield and on-time delivery remain consistent and trustworthy across all dashboards and decision systems.
• Collaborate closely with manufacturing and operations teams to translate business requirements into robust analytical models and data structures.
• Implement data modeling best practices that support both real-time operational intelligence and long-term strategic analysis.
• Contribute to the design of data products that serve both human decision-makers and automated systems within the factory environment.

Requirements

• Bring production ownership of data models with years of experience that scale commensurately with the level of responsibility.
• Demonstrate expert SQL capabilities, including window functions and CTEs, with a real grasp of query performance and cost in large-scale environments.
• Ship production data pipelines using Spark, dbt, and Dagster or equivalent orchestration tools with a focus on reliability and maintainability.
• Possess a strong data-modeling foundation, including normalization, denormalization, star and snowflake schemas, and dimensional modeling principles.
• Show familiarity with lake and warehouse internals, such as columnar stores, Iceberg catalog structures, partitioning strategies, and materialization patterns.
• Build semantic layers using dbt, Snowflake, or Databricks, with an emphasis on scalability, clarity, and performance.
• Write Python for reusable pipeline components and data application utilities that support analytical workflows.
• End-to-end ownership of analytical deliverables, maintaining a quality bar that prevents stalls in progress and ensures timely delivery.
• Commitment to best practices in data engineering, including version control, testing, monitoring, and documentation throughout the lifecycle.
• Ability to thrive in a fast-paced, mission-critical manufacturing environment where data directly impacts operational decisions.

Nice to have

• Experience building and managing cross-functional data marts and pipelines that integrate multiple business domains.
• Hands-on work with ClickHouse optimization, including materialized views, projections, and time-to-live configurations.
• Knowledge of manufacturing statistics, including Statistical Process Control (SPC), control charts, and process capability analysis.
• Understanding of data mesh and data-product concepts in the context of distributed manufacturing systems.
• Deep expertise in orchestration tools such as Dagster and Airflow for complex data workflows.
• Experience scaling analytics across multiple manufacturing sites or business units with varying data landscapes.
• Background in Operations Research, industrial engineering, or quantitative finance applied to manufacturing contexts.

Practical notes

This role is full-time and based in Los Angeles, California. The position requires standard working hours as determined by team agreements and may involve occasional travel between facilities as needed for alignment and audits. Employment is contingent on eligibility to work in the United States without sponsorship for this position at this time. Applicants must meet the stated requirements without exception, and the hiring process will include interviews designed to assess both technical capability and cultural fit.