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Salary: USD 146,000 - 194,000 / annual
Anduril Industries is a defense technology company transforming military capabilities through advanced technology, AI, autonomy, and sensor fusion. The Supplier Quality Engineering team ensures externally sourced components and assemblies meet rigorous quality standards while driving continuous improvement across the product portfolio.
As a Supplier Quality Data Analytics Engineer, you will be embedded within the Supplier Quality Engineering organization as the team's dedicated analytics partner. You will build and maintain data infrastructure, dashboards, and automated workflows that provide real-time visibility into supplier performance, non-conformance trends, corrective action effectiveness, and incoming inspection results. Using Palantir's Foundry platform as your central resource, you will conduct ad-hoc analysis, produce interactive dashboards, and develop automated workflows that write back to systems of record.
Key responsibilities include:
- Transform raw ERP and QMS data extractions into structured datasets using SQL, focusing on supplier quality metrics (PPM, DPMO, SCAR cycle time, lot acceptance rates)
- Generate dashboards and automated workflow code repositories using Palantir Foundry for supplier quality reporting
- Build and maintain supplier scorecards aggregating quality, delivery, and responsiveness data for supplier review boards
- Define and implement key performance metrics (supplier PPM, SCAR closure rate, first pass yield) with systems for tracking over time
- Develop automated alerting and escalation workflows for non-conformance trends, repeat defects, and at-risk suppliers
- Eliminate manual processes such as inspection data compilation, SCAR status tracking, and supplier report generation through programmatic automation
- Manage cross-functional projects with supply chain analytics, production quality, and engineering teams
- Assess and incorporate external data sources for supplier quality risk management
- Educate supplier quality engineers on analytics tools and drive user adoption
- Embed AI into workflows via Palantir Foundry to identify patterns, predict supplier risk, and extract signal from noise
You will gain deep understanding of supplier quality processes—from incoming inspection and SCAR management to supplier audits and approved supplier list governance—and craft tools that drive measurable improvements in outcomes.
REQUIREMENTS:
- Bachelor's degree in Analytics, Data Science, Computer Science, Industrial Engineering, Quality Engineering, or related technical field
- 2+ years of experience in analytics, data engineering, quality engineering, or operations engineering roles, preferably in complex hardware technology or manufacturing industries
- Expertise in SQL with demonstrated ability to write complex queries across multiple data sources
- Expertise in data visualization platforms such as Tableau, Looker, Power BI, Streamlit, Dash, or Palantir Foundry
- Strong proficiency in Microsoft Excel including advanced formulas, live database connections, and VBA
- Proficient in at least one additional programming language such as Python, R, Typescript, or similar for analysis, data visualization, and automation
- Ability to relocate or be local to Costa Mesa, CA
- Ability to travel up to 10% of the time
PREFERRED QUALIFICATIONS:
- Experience with supplier quality concepts such as incoming inspection, SCAR/CAPA management, supplier audits, approved supplier list (ASL) governance, and statistical process control (SPC)
- Familiarity with quality management systems (QMS) and standards such as AS9100, ISO 9001, or IATF 16949
- Experience working with ERP/MRP systems (SAP, Oracle, NetSuite, or similar) and QMS platforms
- Working knowledge of Git/GitHub for version control
- Experience with APIs and parsing JSON/XML responses into structured datasets
- Understanding of statistical methods for quality analysis (Pareto, control charts, capability studies, hypothesis testing)
- Ability to take projects from ideation to implementation that drives tangible business impact
- Continual desire to learn and use new technologies/software
