Data Engineer, Anduril Industries

• 5+ years of experience in a data engineering or a similar software engineering role, ideally building data products in a fast-paced environment
• Strong programming proficiency in Python
• Hands-on expertise with at least one major cloud data platform such as Google BigQuery, Databricks, or an enterprise data system like Palantir Foundry
• Expert-level SQL and query optimization skills on large-scale datasets
• Solid understanding of data modeling concepts, ETL/ELT design patterns, and data warehousing/lakehouse principles
• Experience with a major cloud environment (GCP, AWS, Azure), including its security ecosystem and containerization technologies (Docker, Kubernetes)
• Must be eligible to obtain and maintain a US Top Secret security clearance

Desirable

• Anduril’s Infrastructure Engineering organization provides the digital foundations for all aspects of our business. We are evolving into a more data-driven approach for designing, building, and managing the critical infrastructure that ingests, transforms, and serves massive datasets to power analytics, machine learning, and intelligent applications
• We are seeking a highly skilled Data Engineer with a strong software engineering foundation to help build and scale our next-generation data platform. This is a high-impact role where you will have the autonomy to architect robust systems that unlock critical business insights from complex, large-scale data
• Design and Build Scalable Data Pipelines: Architect, develop, and maintain robust and efficient ETL/ELT pipelines to process petabyte-scale data from a wide variety of sources using platforms like Google BigQuery , Databricks, and Palantir Foundry
• Apply Software Engineering Principles: Treat data infrastructure as a software product. Develop reusable frameworks, libraries, and tools to accelerate data pipeline development and ensure reliability , using best practices like version control, code reviews, and CI/CD
• Architect and Model Data: Design and implement well-structured, performant data models and schemas in our data warehouse and data lakehouse environments, optimized for both analytical querying and application use
• Ensure Data Quality and Reliability: Implement comprehensive data quality checks, monitoring, and alerting to proactively identify and resolve data issues. Troubleshoot and optimize performance bottlenecks in data processing and storage layers
• Be a Force Multiplier: Collaborate closely with data scientists, analysts, software engineers, and business stakeholders