Data Engineer, Quality Intelligence

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.ABOUT ANDURIL Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the pace, focus, and product-driven culture of Silicon Valley to the defense sector, Anduril is building software and hardware systems that solve the most urgent national security challenges.    ABOUT THE TEAM Quality Intelligence is a growing, high-leverage HQ team that builds the data analytics and AI that make Anduril's manufacturing programs measurably better. Our customers are program quality leaders, manufacturing engineers, and operators across sites and programs. The work is concrete: dashboards that catch quality drift before customers do, pipelines that turn ERP/MES/QMS information into decisions, and AI tools that compress hours of manual triage into minutes.    ABOUT THE ROLE This is a builder role. You will own end-to-end data and analytics work that shows up directly on a factory floor. You'll pull data from real production systems (ERP, MES, QMS, Inventory), shape the data into pipelines and ontologies in Palantir Foundry and Databricks, and partner with engineers, ML practitioners, and program quality leads to ship analytics products people actually 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.  You will also be expected to be near the hardware. Manufacturing is a building full of people who need answers from your data, and the best engineers on this team are the ones who walk the line, ask "what is this part," and let that shape the schema.    WHAT YOU'LL DO Support & Maintain Data Analytics: Support analytics and investigations utilizing existing tools and capabilities. Direct users to utilize analytic dashboards, troubleshoot existing dashboard accuracy, and provide modifications and improvements to increase dashboard effectiveness. Investigate Data Quality: Act as a lead technical investigator for data quality issues. When a dashboard is inaccurate or data seems wrong, you will perform deep-dive analysis using SQL and Python to trace the problem back to its source and identify the root cause. Troubleshoot & Optimize: Support the analytics team by troubleshooting data access issues, improving pipeline performance for faster dashboard loads, and ensuring the overall health and reliability of the quality data ecosystem in Foundry. Drive Technical Improvements: Implement robust data quality checks, validation rules, and automated monitoring directly within data pipelines to proactively prevent future data quality issues and eliminate variance. Enable Self-Service Analytics: Partner closely with cross-functional teams and ML Engineers to understand current and future data needs. Build clean, reliable, and well-structured datasets that allow teams to independently create reliable dashboards, reports, and models. Lead Data Projects: Collaborate with partner teams on analytics initiatives from requirements gathering through deployment. Explore efficient ways to deliver the ask, partnering with Data Scientists and other analysts to deliver high-impact, well-rounded solutions. Build AI-Assisted Tools: Develop small apps and workflows (often in Foundry Workshop / AIP) that reduce repetitive analyst work by 10x, leveraging operations knowledge gained directly from conversations with your stakeholders. Capability Discovery: Partner with program quality leads who don't yet know what Quality Intelligence can do for them. Translate their operational problems into data products that already exist or can be configured quickly.   WHO YOU ARE AI-First Builder: You use AI as part of your daily workflow—Cursor, Claude Code, Copilot, AIP, or whatever fits the task. You review AI-generated code critically, apply it thoughtfully, and always double-check its output. Hands-on & Curious: You are comfortable on the factory floor. You'd rather spend an hour with a manufacturing engineer reviewing a part than guess at column names from your desk. Impact-Driven: You measure your work by what ships and gets used. You'd rather own a small set of pipelines that operators depend on than a wide backlog that no one is asking for. Technical & Pragmatic: You write SQL and Python regularly and readily understand code generated by AI. You can read another engineer's pipeline, identify weaknesses, and suggest concrete improvements. Clear Communicator: You communicate plainly. You can explain a complex data anomaly to a director without jargon, and to an engineer without losing precision.   REQUIRED QUALIFICATIONS Bachelor’s degree in Data Engineering, Computer Science, Statistics, Engineering, or a related technical field—or equivalent practical experience.  3+ years in a hands-on data role: Data Engineer, Analytics Engineer, BI Engineer, or similar. Production experience with Foundry, Databricks, Snowflake + dbt, or an equivalent cloud lakehouse—you have built and maintained pipelines that other teams depend on. Strong SQL skills on large, multi-source datasets (joins across heterogeneous systems, window functions, and performance tuning). Strong Python skills for data transformation