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Hadrian

Data Science/ Data Engineer Intern

Los Angeles, CAfulltimeinternAdded today

About this role

Join Hadrian's data team as an intern to build the data infrastructure powering autonomous factories. You'll work with real production data from the factory floor, developing pipelines, analyses, and dashboards that directly influence manufacturing operations and decisions.

What you'll do

  • Build and maintain ETL pipelines moving machine telemetry, production, and quality data into the warehouse
  • Model and structure datasets for engineering, operations, and quality team decision-making
  • Analyze production metrics including throughput, capacity, cycle time, yield, and machine utilization
  • Develop dashboards and reporting to improve factory performance visibility
  • Prototype statistical and machine learning approaches for anomaly detection and process optimization
  • Collaborate cross-functionally to translate business questions into measurable analyses

What they're looking for

  • Python
  • SQL
  • Data pipeline and ETL/ELT workflows
  • Cloud data warehouses
  • Statistics and data analysis
  • Dashboard and visualization tools
  • Time-series or sensor data experience
  • Clear technical communication

Benefits

  • Hands-on experience with real production data at scale
  • Direct impact on operational decisions at a high-growth company
  • Exposure to manufacturing systems and industrial automation
  • Mentorship from experienced data engineers and analysts
  • On-site role in Los Angeles with mission-driven team
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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 built or improved a data pipeline—what were the key challenges and how did you handle data quality issues?
  • Describe your experience with ETL/ELT tools and cloud data warehouses. Which have you used and why?
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