Analytics Lead, Manufacturing QualityActive$218K

Anduril Industries is a defense technology company with a mission to transform U. S.

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 analyzing quality workflows, building analytics tools for said 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.

• Develop & Operate Analytics for Arsenal 1: You will design, build, and operate the production dashboards, pipelines, and quality metrics inspection-data analytics the site runs on. Arsenal 1 is greenfield, so you will collaborate closely with HQ to port over proven work. Well thought out decisions you make set the pattern for future programs.

• Run intake and priorities for the site: hold a standing feedback loop with operators, manufacturing engineers, and program quality. Turn what you hear into a prioritized, visible backlog — what you can configure this week, what needs HQ build time, and what we are deliberately not doing — 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 are the lead investigator. Deep-dive analysis in SQL and Python, trace problems through the stack, identify the root cause, and fix it at the source.

• 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.

What they're looking for

• 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 own the rollout.
• Drive adoption: a dashboard nobody opens is ineffective. You will train operators, run office hours, track usage, and treat adoption at Arsenal 1 as a deliverable you own, not a downstream side effect.
• AI Use: You will be expected to use AI aggressively in your own work: to draft pipelines, write tests, generate dashboards, explore unfamiliar data, and accelerate the repetitive parts of the job.