Data Analyst, QualityNew$170K

Anduril Industries
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The opportunity

Quality Intelligence supports data and AI in Anduril's manufacturing quality organization through three distinct areas: Analytics, Manufacturing AI, and Vision Inspection. Analytics owns end-to-end data and analytics work that directly drives impact for Anduril's product quality engineers on the factory floor.

What you'll do

  • Intersection of Analytics and Manufacturing: You will operate at the intersection of hardware manufacturing and data analytics. You are not afraid to spend time on the shop floor understanding quality workflows, building analytics tools for those workflows, and implementing them in well-designed, actionable dashboards.

  • 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 program quality leads to ship analytics products operators depend on.

  • 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. Help turn what you hear into a prioritized, visible backlog and file crisp, evidence-backed asks back to HQ so the platform gets better.

  • 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. Your data is trusted because you helped make it provably trustworthy.

What they're looking for

  • 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.
  • Drive Adoption: A dashboard nobody opens is ineffective. You will help train operators, run office hours, track usage, and treat adoption as a deliverable, not a downstream side effect.
  • AI Use: You will be expected to use AI in your own work: to draft pipelines, write tests, generate dashboards, explore unfamiliar data, and accelerate the repetitive parts of the job.