Data Engineer

Why Leo Labs?

At Leo Labs, we’re building the living map of activity in space. Through our proprietary global radar network and AI-enabled analytics platform, we collect millions of measurements daily on more than 25,000 objects in low Earth orbit (LEO). Our radar-powered intelligence protects billions in assets, monitors adversarial behavior, and ensures safe operations for commercial and government missions.

We’re not just building technology, we are redefining global security, safety, and transparency in space. As orbital activity accelerates and threats grow more complex, Leo Labs is a trusted partner for Space Domain Awareness, Space Traffic Management, and Satellite Operations for top-tier space operators and allied defense organizations.

If you're looking to work on mission-critical challenges at the forefront of aerospace, national security, and AI, your impact starts here.

We are seeking a motivated and hands-on early career Data Engineer to join Leo Labs’ growing Insights team. You will play a key role in building and operating the data pipelines and analytics infrastructure that power customer insights, internal decision-making, and real-time space domain awareness capabilities.

You will work closely with software engineers, radar and catalog teams, and data scientists to ensure reliable extraction, transformation, and loading (ETL) of mission-critical datasets. This includes developing scalable batch and streaming data workflows, enabling advanced analytics, and supporting machine learning initiatives.

Your contributions will help transform large volumes of sensor and orbital data into actionable intelligence that enables users to safely operate and manage assets in low‑Earth orbit. This early career role is primarily hands‑on development, with opportunities to grow into increased ownership of data platform design and optimization.

• Must be eligible to obtain and maintain a U.S. personnel security clearance
• B.S. or M.S. in Computer Science, Data Science, Engineering, Mathematics, Physics, or equivalent experience
• 0‑2 years of experience in data engineering, software engineering, analytics engineering, or related technical roles
• Experience designing and building data pipelines or ETL/ELT workflows
• Hands‑on experience with Databricks, Apache Spark, or distributed data processing frameworks
• Proficiency in Python and SQL for data transformation and analysis
• Familiarity with data modeling concepts and modern data lake or warehouse architectures
• Experience working in cloud‑native environments (AWS preferred)
• Understanding of software development best practices including version control, testing, and CI/CD
• Strong analytical mindset and ability to troubleshoot complex data issues
• Effective communication skills and ability to collaborate across distributed engineering teams
• Ability to participate in operational support rotations during critical incidents

• Experience supporting data science or machine learning workflows, including feature engineering pipelines
• Familiarity with Delta Lake, Lakehouse architectures, or large‑scale telemetry data processing
• Exposure to streaming data systems such as Kafka or Spark Structured Streaming
• Experience with workflow orchestration tools such as Airflow or Databricks Workflows
• Background in orbital mechanics, aerospace, physics, or applied mathematics
• Experience building analytics datasets or semantic models for BI tools
• Active U.S. security clearance or ability to obtain one

• Complete onboarding to gain familiarity with Leo Labs’ mission, products, and data platform
• Successfully configure development environments, tooling, and data access
• Participate in team ceremonies and begin ramping up on existing data pipelines and platform services
• Deliver initial low‑risk improvements such as pipeline enhancements, monitoring updates, or documentation contributions

• Develop working knowledge of Leo Labs’ data architecture, ingestion systems, and Databricks workflows
• Contribute to building and maintaining small‑to‑medium data pipeline features
• Collaborate with data scientists and product…