Data Engineer, Quality Intelligence Software

Data Engineer, Quality Intelligence Own end-to-end data pipelines and analytics for manufacturing quality on the factory floor Location: Costa Mesa, California, United States Compensation: $144,000 - 191,000 USD / year Job Tags: Software 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‑diving 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—use AI as part of your daily workflow—Cursor, Claude Code, Copilot, AIP, or whatever fits the task. Review AI‑generated code critically, apply it thoughtfully, and always double‑check its output. Hands‑on & Curious—comfortable on the factory floor. Prefer an hour with a manufacturing engineer reviewing a part rather than guessing column names from your desk. Impact‑Driven—measure your work by what ships and gets used. Own a small set of pipelines operators depend on rather than a wide backlog that no one is asking for. Technical & Pragmatic—write SQL and Python regularly and readily understand code generated by AI. Can read another engineer's pipeline, identify weaknesses, and suggest concrete improvements. Clear Communicator—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—built and maintained pipelines teams depend on. Strong SQL skills on large, multi‑source datasets (joins across heterogeneous systems, window functions, performance tuning). Strong Python skills for data transformation and scripting (Pandas, PySpark, or equivalent). Demonstrated ability to perform root‑cause analysis on complex data issues—trace a wrong number through the stack and explain why. Comfort using AI coding tools in your daily workflow, with a clear view of where they help and where they don't. Willingness to work in and around manufacturing operations, including periodic time on the production floor and at remote sites. Strong cross‑functional communication skills—substantive conversations with engineers, non‑technical operators, and program leadership. Must be a U.S. Person (U.S. Citizen or Permanent Resident) due to ITAR/export control regulations. 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 performance. Strong software engineering practices: Git, code reviews, CI, testing data code with the same rigor as application code. Experience integrating LLMs or ML models into analytics workflows (e.g., RAG over operational data, AI‑assisted triage, or agentic data exploration). Comfort with standard collaboration tools: Slack, Jira, Confluence. A Day In The Life 9:00 AM: Standup with the QI team in Costa Mesa; a teammate flags a scorecard regression from overnight, and you pick up the investigation. 10:30 AM: Pairing with a manufacturing engineer on the production floor to resolve a part the data is mis‑classifying—you leave with new test cases for your pipeline.

1. 00 PM: Refactoring an ontology object that has grown unwieldy, using Claude or Cursor to accelerate the change. Ship behind a feature flag.
2. 30 PM: Reviewing the Piece Part Plan onboarding flow with a program quality lead and scoping how it could apply to their program.
3. 30 PM: Focused work—instrumenting a new data‑quality check in a Foundry pipeline to catch a class of bad‑supplier rows you noticed last week.

5:00 PM: Writing a quick Confluence summary so the next person on the team does not have to relearn what you just figured out. Location & Travel Location: Costa Mesa, CA (On‑site) Travel: ~10–20% to manufacturing and remote sites. U.S. Person #J-18808-Ljbffr jobs.frontdoordefense.com - Jobboard