Senior Data Analyst, Quality

ABOUT THE TEAM

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. Our customers are program quality leaders, manufacturing engineers, and operators across sites and programs. The work encompasses building scorecards that catch quality drift before customers do, pipelines that turn ERP / MES / QMS data into decisions, and AI tools that compress hours of manual triage into minutes.

We operate hub-and-spoke. HQ builds platform-grade analytics centrally; site engineers localize and run them at each manufacturing site. This is the site analytics lead role at our Atlanta facility. You are the resident analytics engineer and the person accountable for what Quality Intelligence has deployed here. Atlanta already has production dashboards and pipelines running; you will own them, keep them accurate, and improve them as programs evolve. Beyond maintaining what exists, you will build new analytics products as the site takes on new programs and requirements from the floor. Most of this role is hands-on build; the rest is running the site as a program: gathering requirements from operators and engineers, sequencing the work, driving rollouts to completion, and feeding what you learn back into the HQ roadmap.

This role is subject to ITAR. Applicants must be eligible to obtain and maintain a U.S. Government security clearance.

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 and Operate Site Analytics: You will design, build, and operate the production dashboards, pipelines, and quality metrics inspection-data analytics the site runs on. 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.
• 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 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.

REQUIRED QUALIFICATIONS

• 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 2 years applied data engineering experience in a manufacturing or hardware product engineering environment.
• You understand how manufacturing works: the workflows, the quality gates, and how manufacturing data drives or degrades quality outcomes. You are willing to work in and around manufacturing operations, including time on the production floor.
• Production 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. You are aware of the mistakes AI tools can make, with a clear view of where they help and where they don't.
• Strong Python for data transformation and scripting (Pandas, PySpark, or equivalent).
• 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).
• You communicate plainly: to a director without jargon, to an engineer without losing precision.
• Travel up to 25% to Anduril sites and vendors.
  
  

PREFERRED QUALIFICATIONS

• Experience supporting analytics for hardware manufacturing (NPI, ramp, high-volume) across any of ERP (Oracle, NetSuite, SAP), MES, QMS, PLM (Teamcenter), or inventory / warehouse systems.
• Experience as an embedded or site-resident engineer at a manufacturing or industrial site, or standing up systems and data at a greenfield facility.
• Familiarity with quality methodologies: RCCA / 8D, FMEA, GD&T, IQC / OQC, control-plan design.
• Defense or regulated-manufacturing experience (ITAR, AS9100, IPC-610, MIL-STD-1916, or similar).
• Software engineering practices: Git, code review, CI, and testing data code with the same rigor as application code.
• Experience integrating LLMs or ML models into analytics workflows: RAG over operational data, AI-assisted triage, or agentic data exploration.
• Experience mentoring or leading a small team of engineers or analysts.