ABOUT THE TEAMThe Supplier Quality Engineering team is responsible for ensuring that externally sourced components and assemblies meet Anduril's rigorous quality standards. The team works closely with suppliers, production, and engineering to drive corrective actions, monitor supplier performance, and continuously improve incoming material quality across Anduril's product portfolio. As the organization scales rapidly, data-driven decision making is critical to identifying trends, prioritizing supplier interventions, and measuring the effectiveness of quality programs.
ABOUT THE JOBAs a Supplier Quality Data Analytics Engineer, you will be embedded within the Supplier Quality Engineering organization and serve as the team's dedicated analytics partner. You will build and maintain the data infrastructure, dashboards, and automated workflows that give supplier quality engineers 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 our systems of record. You will gain a 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 supplier quality outcomes.
WHAT YOU'LL DO- Use SQL to transform raw ERP and QMS data extractions into structured datasets within a centralized GitHub repository, with a focus on supplier quality metrics (PPM, DPMO, SCAR cycle time, lot acceptance rates)
- Use Palantir's Foundry platform to generate dashboards and automated workflows code repositories for supplier quality reporting
- Build and maintain supplier scorecards that aggregate quality, delivery, and responsiveness data to support supplier review boards and business decisions
- Work with Supplier Quality Engineers and managers to define key performance metrics (e.g., supplier PPM, SCAR closure rate, first pass yield) and implement systems for tracking over time
- Develop automated alerting and escalation workflows for non-conformance trends, repeat defects, and at-risk suppliers
- Eliminate repetitious processes - such as manual inspection data compilation, SCAR status tracking, and supplier report generation - with programmatic automation
- Manage cross-functional projects with clear and concise communication on timelines, partnering with supply chain analytics, production quality, and engineering teams
- Assess and incorporate external data sources that could bring valuable insight to supplier quality risk management
- Educate supplier quality engineers with training and documentation on analytics tools, and drive user adoption through a continual feedback loop
- Embed AI into workflows via Palantir's Foundry platform to help identify patterns in non-conformance data, predict supplier risk, and drive signal from noise
REQUIRED QUALIFICATIONS- 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, with ability to extract and analyze data from these systems
- 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 a project from ideation to an implementation that drives tangible business impact
- Continual desire to learn and use new technologies/software inside and outside of work
US Salary Range
$146,000-$194,000 USD
The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:
BenefitsAt Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you're supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits.
