Description
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology.
You will be working as a Data Analyst, Quality in the Quality Intelligence team, supporting data and AI in Anduril's manufacturing quality organization.
Responsibilities:
- Operate at the intersection of hardware manufacturing and data analytics
- Pull from production systems (ERP, MES, QMS, inventory), build pipelines and ontologies in Palantir Foundry and Databricks
- Build and maintain site analytics, production dashboards, pipelines, and quality metrics
- Support intake and priorities for the site, participate in a standing feedback loop with operators, manufacturing engineers, and program quality
- Investigate data quality issues, perform deep-dive analysis in SQL and Python
- Contribute to technical improvements, implement data-quality checks, validation rules, and automated monitoring
- Build AI-assisted analytics tools, small apps, and workflows in Foundry / Databricks
- Support data projects end-to-end, partner with cross-functional teams from requirements through deployment
- Drive adoption, help train operators, run office hours, track usage
- Use AI in your own work, to draft pipelines, write tests, generate dashboards
Requirements:
- Bachelor's degree in Computer Science, Mechanical Engineering, Industrial Engineering, or a related technical field
- 2+ years in a Data Analyst, Data Engineer, Analytics Engineer, or similar role
- Experience with Foundry, Databricks, or an equivalent cloud lakehouse
- Strong SQL on large, multi-source datasets
- Applied experience using AI tools
- Demonstrated ability to troubleshoot data issues
- Eligible to obtain and maintain a U.S. Government security clearance
- Plain communication style
- Travel up to 25% to Anduril sites and vendors
Preferred Qualifications:
- Experience in a hardware manufacturing environment
- Familiarity with manufacturing data systems
- Strong Python for data transformation and scripting
- Familiarity with quality methodologies
- Defense or regulated-manufacturing experience
- Software engineering practices
- Experience integrating LLMs or ML models into analytics workflows