Anduril Industries
Mission Analytics Engineer
About this role
Anduril Industries seeks a Mission Analytics Engineer to design and maintain data systems supporting the Air Defense team's mission-critical operations. You'll build data pipelines, create analytics solutions for both classified and operational environments, and partner with engineering and field teams to deliver actionable insights that drive decision-making.
What you'll do
- Design and maintain data ingestion systems for structured, semi-structured, and unstructured data from multiple sources
- Develop secure, reliable data models and transform data for downstream analytics and operational workflows
- Collaborate with stakeholders to gather requirements and build dashboards and metrics for decision support
- Develop analytics solutions for network-isolated and classified environments
- Ensure data quality, perform root cause analysis, and support operational teams with data products
- Travel occasionally to deploy and test capabilities in real-world scenarios
What they're looking for
- Data engineering and analytics (3+ years experience)
- Cloud environment data development
- Multiple programming languages and frameworks
- Problem-solving and critical thinking
- Technical documentation and communication
- Python, C/C++, or other systems programming languages (preferred)
- Modern data tools (dbt, Spark, Kafka, Flink, etc.)
- Data handling in air-gapped and security-hardened networks (preferred)
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Anduril Industries
Anduril Industries builds autonomous defense systems including underwater vehicles, unmanned aircraft, and electronic warfare platforms for the Department of Defense. The company is hiring across mechanical engineering, mission operations, software development, technical leadership, and advanced manufacturing roles to support the design, deployment, and production of these mission-critical systems.
- Website
- anduril.com
Likely interview questions
- Walk us through a data ingestion and transformation project you've built. How did you handle data quality issues, and what was your approach to ensuring low-latency delivery?
- Describe your experience working with cloud environments for analytics. What tools and frameworks have you used, and how did you optimize for performance and cost?