Data Analyst, QualityPosted today$120K

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 work at the intersection of hardware manufacturing and data analytics. You are not afraid to spend time on the shop floor learning quality workflows, building analytics tools, and turning what you observe into well-designed, actionable dashboards.

• Production Data: You'll pull from production systems (ERP, MES, QMS, inventory), help 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 & Maintain Analytics for Arsenal-1: You will help design, build, and maintain the production dashboards, pipelines, and quality metrics the site runs on. Arsenal-1 is greenfield, so you will collaborate closely with HQ to port over proven work and adapt it to site-specific needs.

• Support intake and requirements: participate in feedback loops with operators, manufacturing engineers, and program quality. Help translate what you hear into backlog items -- what can be configured this week, what needs HQ build time, and what we are deliberately not doing.

• Investigate data quality: When a dashboard is inaccurate or a number looks wrong, dig in. Run deep-dive analysis in SQL and Python, trace problems through the stack, identify the root cause, and work with the team to fix it at the source.

• Contribute to technical improvements: Help implement data-quality checks, validation rules, and automated monitoring directly in the pipelines. Your data is trusted because it is 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 rollout efforts.
• Drive adoption: a dashboard nobody opens is ineffective. You will help train operators, run office hours, track usage, and treat adoption at Arsenal-1 as a deliverable, 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.