Senior Data Analyst, Quality

Job description

Job responsibilities

• Intersection of Analytics and Manufacturing: You will operate at the intersection of hardware manufacturing and data analytics.
• Production Data: You'll pull from production systems (ERP, MES, QMS, inventory), build pipelines and ontologies in Palantir Foundry and Databricks, and partner with manufacturing engineers, ML practitioners, and…
• Develop and Operate Site Analytics: You will design, build, and operate the production dashboards, pipelines, and quality metrics inspection-data analytics the site runs on.
• Run intake and priorities for the site: Hold a standing feedback loop with operators, manufacturing engineers, and program quality.
• Investigate data quality: When a dashboard is inaccurate or a number looks wrong, you are the lead investigator.
• Drive technical improvements: You will implement robust data-quality checks, validation rules, and automated monitoring directly in the pipelines. Your data is trusted because you made it provably trustworthy.
• Build AI-Assisted Analytics Tools: Small apps and workflows in Foundry / Databricks that reduce repetitive analyst work by 10x, grounded in what you have learned from operators on the floor.
• Lead Data Projects End-to-End: Partner with cross-functional teams from requirements through deployment. Translate program quality leads' problems into data products that already exist or can be configured quickly and…

Job requirements

• Bachelor's degree in Computer Science, Mechanical Engineering, Industrial Engineering, or a related technical field from an accredited engineering program.
• 4+ years in a Data Engineer, Analytics Engineer, or similar role with at least 1 year applied data experience in hardware product engineering environment.
• You understand how manufacturing works: the workflows, the quality gates, and how manufacturing data drives or degrades quality outcomes.
• Experience with Foundry, Databricks or an equivalent cloud lake house. You have built and maintained pipelines and dashboards other teams depend on.
• Strong SQL on large, multi-source datasets: joins across heterogeneous systems, window functions, and performance tuning.
• Strong applied experience using AI (Cursor, Claude Code, Copilot, AIP) with ability to review AI-generated artifacts critically.
• Demonstrated root-cause analysis on complex data issues. When a number looks wrong, you can trace it back through the stack and explain why.
• Eligible to obtain and maintain a U.S. Government security clearance (this role is subject to ITAR).