Data Engineer, SandboxAQ

• US citizenship (required for working with CUI data)
• 3+ years of industry experience as a Data Engineer in a startup or fast-moving environment
• Strong proficiency in Python and SQL, with hands-on experience building production-grade data solutions
• Experience designing and maintaining data pipelines and data models/warehouses that process large, structured scientific or engineering datasets
• Hands-on experience building on AWS (e.g., S3, ECS, Lambda, IAM) combined with CI/CD and containerization (e.g., GitHub Actions or CircleCI, Docker) to automate, deploy, and maintain data and ML workloads in the cloud
• Practical MLOps experience: setting up and operating MLOps frameworks (e.g., MLFlow, DVC)

Desirable

• SandboxAQ's AQNav (Quantum Navigation) team builds global, GPS-independent, navigation solutions from military and commercial aircraft to autonomous submersible platforms
• We are the bridge between cutting-edge quantum sensing research and real-world operations, working directly alongside the defense customers and platform integrators at the highest levels to prove that magnetic navigation works where it matters most
• The AQNav team is looking for a highly-accomplished Data Engineer to help take us to the next level
• This role is that role you always wanted, to be able to work on cutting-edge technology
• You get to join and help build out infrastructure that will empower every member of the team with the data they need, pipelines to help them move faster, and processes that will accelerate and validate our models
• The tools and pipelines you help build will directly impact the team's success
• We are looking for a data engineer that thrives wearing a variety of hats and enjoys working alongside a diverse group including physicists, ML researchers, hardware engineers, software engineers, data engineers, field ops personnel, navigation engineers, customer facing leaders, and managers at different levels
• Data Pipeline Development & Maintenance: Work across a mixed-maturity pipeline environment
• Data Modeling: Build and optimize data models that serve a diverse set of consumers. You'll make the data accessible and trustworthy, not just available
• Simulation Data Integration: Work within the in-house simulation suite to add data-capturing capabilities and ensure simulation outputs feed cleanly into downstream pipelines alongside real-world field data
• Data Quality & Observability: Instrument pipelines with quality checks, anomaly detection, and alerting so issues surface early
• Cross-Functional Data Support: Translate ambiguous asks into well-defined requirements, repeatable datasets and lightweight Dashboards that the team can use independently going forward
• Data Platform Infrastructure Contribution: Improve the features and reliability of our internal data platform over time
• Documentation: Own the technical documentation for pipelines, data models, and schemas you touch. In a team this cross-functional, good documentation is a force multiplier