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Salary: USD 97,000 - 120,000 / annual
Anduril Industries is a defense technology company building Arsenal-1, a hyperscale autonomous systems and weapons manufacturing facility south of Columbus, Ohio. The Quality Intelligence team supports manufacturing quality through analytics, manufacturing AI, and vision inspection.
You will serve as the resident analytics engineer at Arsenal-1, accountable for deploying and maintaining Quality Intelligence analytics across the site. This is a hands-on role combining data engineering, analytics, and manufacturing operations. You will work at the intersection of hardware manufacturing and data analytics, spending time on the shop floor to understand quality workflows, then building dashboards and tools that operators depend on.
Key responsibilities include:
- Design, build, and maintain production dashboards, pipelines, and quality metrics for Arsenal-1, adapting proven HQ work to site-specific needs
- Pull data from production systems (ERP, MES, QMS, inventory) and build pipelines in Palantir Foundry and Databricks
- Gather requirements from manufacturing engineers, program quality leads, and operators; translate feedback into backlog items and prioritize work
- Investigate data quality issues by running deep-dive SQL and Python analysis, tracing problems through the stack, and fixing root causes
- Implement data-quality checks, validation rules, and automated monitoring in pipelines
- Build AI-assisted analytics tools and small apps that reduce repetitive analyst work
- Support data projects end-to-end from requirements through deployment and rollout
- Drive adoption by training operators, running office hours, tracking usage, and treating adoption as a deliverable
- Use AI aggressively in your own work—drafting pipelines, writing tests, generating dashboards, exploring unfamiliar data
- Collaborate with cross-functional teams, including regular interaction with production stakeholders and work on the production floor
Arsenal-1 is a greenfield site and a lighthouse facility; patterns you establish here become the template for future factories.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Data Science, Industrial Engineering, Mechanical Engineering, or related technical field. Recent graduates (0-2 years experience) encouraged to apply.
- Demonstrated interest or coursework in data engineering, analytics, or applied data science through internships, co-ops, capstone projects, or personal projects
- Curiosity about manufacturing workflows, quality gates, and how data drives quality outcomes; willingness to work on the production floor
- Proficiency in SQL: comfortable writing queries across multiple tables using joins, aggregations, and window functions
- Familiarity with at least one programming language for data work (Python preferred)
- Comfort using AI tools (Cursor, Claude Code, Copilot, ChatGPT) and ability to review AI-generated output critically
- Strong problem-solving instincts; when a number looks wrong, your impulse is to figure out why
- Eligible to obtain and maintain a U.S. Government security clearance (role subject to ITAR)
- Clear communication skills: to directors without jargon, to engineers without losing precision
- Based in or willing to relocate to greater Columbus, OH area; work on-site at Arsenal-1 in Ashville, OH
- Travel up to 25% to Anduril sites and vendors
- U.S. citizenship required per contractual obligations
PREFERRED QUALIFICATIONS:
- Internship or co-op experience in manufacturing, industrial, or hardware engineering
- Exposure to ERP (Oracle, NetSuite, SAP), MES, QMS, PLM (Teamcenter), or inventory/warehouse systems
- Experience with cloud data platforms (Databricks, Foundry, Snowflake, BigQuery, or similar)
- Python for data transformation and scripting (Pandas, PySpark, or equivalent)
- Familiarity with quality methodologies: RCCA/8D, FMEA, GD&T, IQC/OQC, or statistical process control
- Defense or regulated-manufacturing exposure (ITAR, AS9100, IPC-610, MIL-STD-1916, or similar)
- Software engineering practices: Git, code review, CI, and testing data code with same rigor as application code
- Interest in integrating LLMs or ML models into analytics workflows
