Senior Data Engineer, Quality Intelligence

About Quality Intelligence

Quality Intelligence 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: 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 a hub-and-spoke model. HQ builds platform-grade analytics centrally; site engineers localize and run them at each manufacturing site. This role anchors the build at our Atlanta site.

 

About the Role

This is a senior, hands-on data engineer role at Anduril's Atlanta manufacturing operations. You will own end-to-end data and analytics work that shows up directly on the factory floor. You'll pull from real 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 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 walk the line, ask “what is this part,” and let that shape the schema.

You will partner directly with the site's applications lead to deliver analytics the production floor depends on. The architectural decisions you make here set the template for our other manufacturing sites.

 

What You'll Do

• Own analytics architecture for the Atlanta site: Design, build, and operate the production scorecards, Piece Part Plan pipelines, and inspection-data analytics the site runs on. Decisions you make set the pattern for other sites.
• Investigate data quality: When a dashboard is inaccurate or a number looks wrong, you are the lead investigator. Perform 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: Implement robust data-quality checks, validation rules, and automated monitoring directly in the pipelines. Your data is trusted because you made it provably trustworthy.
• Enable self-service analytics: Model parts, suppliers, work orders, inspections, and dispositions in Foundry so other teams can build their own dashboards without your help.
• 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.
• Build AI-assisted analytics tools: Develop small apps and workflows in Foundry Workshop / AIP that reduce repetitive analyst work by 10x, grounded in what you have learned from operators on the floor.
• Raise the bar: Review pull requests, run technical interviews, and shape the engineering practices of a growing site team.

 

Who You Are

• Hardware-Minded: You have applied data engineering experience in a manufacturing or hardware product engineering environment. You understand how manufacturing actually runs: the workflows, the quality gates, and how manufacturing data drives or degrades quality outcomes.
• AI-Forward: 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 and apply it thoughtfully.
• Collaborative & Hands-on: 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 nobody asked for.
• Technical: You write SQL and Python every day and have strong opinions about both. You can read another engineer's pipeline, identify weaknesses, and propose concrete improvements.
• Strong Communicator: You communicate plainly: to a director without jargon, to an operator without losing precision.

 

Required Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Engineering, Statistics, or a related technical field from an accredited engineering program.
• 5+ years in a hands-on data role (Data Engineer, Analytics Engineer, or similar), with at least 2 years operating at a senior individual-contributor level.
• Production experience with Foundry, Databricks, Snowflake + dbt, or an equivalent cloud lakehouse. You have built and maintained pipelines other teams depend on, not just queried someone else's.
• Strong SQL on large, multi-source datasets: joins across heterogeneous systems, window functions, and performance tuning.
• 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.
• Comfortable using AI coding tools in daily work, with a clear view of where they help and where they don't.
• Willingness to work in and around manufacturing operations, including time on the production floor.
• U.S. Person status is required as this position needs to access export-controlled data and/or technology.

 

Preferred Qualifications

• Experience supporting analytics for hardware manufacturing (NPI, ramp, high-volume) across any of: ERP (Oracle, NetSuite, SAP), MES, QMS, PLM (Teamcenter, Forge), or inventory / warehouse systems.
• 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).
• Experience building dashboards in Foundry Workshop / Quiver / AIP, Tableau, or PowerBI, with a clear understanding of how upstream data models drive their performance.
• Software engineering best 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.
• Able to obtain and maintain an active U.S. Secret security clearance.