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Anduril Industries, a defense technology company transforming military capabilities through advanced technology, is seeking a Senior Analytics Engineer to join the People Analytics team. This role is central to building and maintaining the robust data infrastructure that powers strategic workforce insights.
You will own the full data lifecycle—from ingesting and integrating diverse HR data sources (HRIS, ATS, LMS) to designing, developing, and optimizing data models and pipelines. Your primary objective is transforming raw, disparate information into clean, reliable, analytics-ready datasets that empower People Analysts and business stakeholders to drive data-driven decision-making across the entire employee lifecycle, from talent acquisition and development to engagement and retention.
Key responsibilities include:
- Design, build, and optimize robust ETL/ELT pipelines to ingest, integrate, and transform people data from various HR systems into the data platform
- Develop, maintain, and govern scalable data models, schemas, and ontologies for people analytics, ensuring data quality, consistency, and accessibility
- Contribute to strategic design and evolution of the people data platform, advocating for engineering best practices and scalable analytics ecosystems (SQLMesh, Iceberg, Flyte)
- Partner with People Analysts, HR Business Partners, and stakeholders to understand analytical needs and translate them into robust data solutions
- Implement and monitor data quality checks, troubleshoot issues, and ensure reliability and integrity of people data
- Monitor pipeline and model performance, identify bottlenecks, and implement efficiency improvements
- Create comprehensive documentation and champion data engineering best practices (version control, testing, CI/CD)
- Implement strict data security measures and ensure compliance with regulations (GDPR, CCPA) related to employee data privacy
- Collaborate with enterprise analytics and data engineering teams to align on data architecture standards
Required qualifications:
- 5+ years of progressive experience in Data Engineering, Analytics Engineering, or similar role building data pipelines and infrastructure
- Expert-level SQL proficiency for complex data manipulation and advanced Python for scripting and automation
- Extensive experience with cloud-based data warehousing (Snowflake, BigQuery, Redshift, Databricks) and data lake technologies (S3, Azure Data Lake)
- Deep understanding of data modeling (dimensional modeling, Kimball methodology)
- Hands-on experience building complex ETL/ELT processes using modern tools (dbt, Apache Airflow, Flyte, Dagster)
- Excellent communication skills, translating technical concepts for non-technical stakeholders
- Bachelor's degree
