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…
• Build and Maintain Site Analytics: You will help design, build, and maintain the production dashboards, pipelines, and quality metrics the site runs on.
• Support intake and priorities for the site: Participate in 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 dig in. Deep-dive analysis in SQL and Python, trace problems through the stack, identify the root cause, and fix it at the source.
• Contribute to technical improvements: You will help implement data-quality checks, validation rules, and automated monitoring directly in the pipelines.
• Build AI-Assisted Analytics Tools: Small apps and workflows in Foundry / Databricks that reduce repetitive analyst work, grounded in what you have learned from operators on the floor.
• Support Data Projects End-to-End: Partner with cross-functional teams from requirements through deployment. Help translate program quality leads' problems into data products and support rollouts.

Job requirements

• Bachelor's degree in Computer Science, Mechanical Engineering, Industrial Engineering, or a related technical field.
• 2+ years in a Data Analyst, Data Engineer, Analytics Engineer, or similar role.
• Experience with Foundry, Databricks, or an equivalent cloud lakehouse. You have built or maintained pipelines and dashboards other teams depend on.
• Strong SQL on large, multi-source datasets: joins across heterogeneous systems, window functions, and performance tuning.
• Applied experience using AI tools (Cursor, Claude Code, Copilot, AIP) with ability to review AI-generated artifacts critically.
• Demonstrated ability to troubleshoot 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).
• You communicate plainly: to a director without jargon, to an engineer without losing precision.