Senior Manager, Data Engineering

True Anomaly•Long Beach, CA

11h•$200,000 - $290,000•Onsite

About The Position

A new space race has begun. True Anomaly seeks those with the talent and ambition to build innovative technology that solves the next generation of engineering, manufacturing, and operational challenges for space security and sustainability. OUR MISSION The peaceful use of space is essential for continued prosperity on Earth—from communications and finance to navigation and logistics. True Anomaly builds innovative technology at the intersection of spacecraft, software, and AI to enhance the capabilities of the U.S., its allies, and commercial partners. We safeguard global security by ensuring space access and sustainability for all. OUR VALUES Be the offset. We create asymmetric advantages with creativity and ingenuity What would it take? We challenge assumptions to deliver ambitious results It’s the people. Our team is our competitive advantage and we are better together YOUR MISSION True Anomaly does not have a central data platform today. Data lives in disconnected systems: PLM, ERP, project management, source control, and documentation tools, with no warehouse, no unified data model, and no programmatic access layer. As Senior Manager of Data Engineering, you will build this capability from scratch: the platform, the pipelines, the team, and the operating model. Your first priority is enabling the AI Platform Engineering team, which depends on a reliable data foundation to deliver on the company’s top strategic priority. Beyond AI, the data platform will serve engineering, programs, manufacturing, and business operations across the company. This is a player-coach role. You will be writing code and making architecture decisions on day one while simultaneously hiring and developing a high-performing team.

Requirements

8+ years of experience in data engineering, data infrastructure, or related fields, with 3+ years building and leading data engineering teams
Strong hands-on skills in modern data stack technologies: data warehousing (Snowflake, BigQuery, Redshift, or similar), ETL/ELT pipelines, data modeling, and orchestration tools (Airflow, Dagster, or similar)
Experience building data platforms from scratch, not just maintaining or extending existing ones
Track record of hiring and developing high-performing engineering teams in fast-paced environments
Ability to work across the full stack of data engineering: from source system connectivity and pipeline reliability to data modeling and serving layer design
Strong communication skills and comfort working with stakeholders across technical and non-technical functions to understand requirements and set priorities
U.S. citizenship required; clearance eligibility required; active clearance at any level preferred

Nice To Haves

Experience in aerospace, defense, or manufacturing environments where data spans engineering tools (PLM, MBSE, CAD/CAM) and enterprise systems (ERP, project management)
Background working with AI/ML teams as a data platform provider, with an understanding of what those teams need from the data layer
Experience connecting complex, heterogeneous source systems with different schemas, update cadences, and access patterns
Familiarity with data governance and data quality practices, and experience building organizational trust in data products
Prior experience at a company poised for aggressive growth where the data platform had to scale ahead of demand
Active U.S. security clearance at any level

Responsibilities

Build the data engineering function from zero: design the data platform architecture, stand up the initial infrastructure, and start delivering value before the team is fully hired
Hire and lead a team of 5 data engineers, recruiting top-tier technical talent who thrive in ambiguous, fast-moving environments
Design and build the company’s data warehouse, unified data model, and pipeline infrastructure, connecting systems like PLM (Teamcenter), ERP (NetSuite), Jira, Git, and Confluence into a single reliable data layer
Partner closely with the AI Platform Engineering team to provide the data infrastructure they need as a critical-path dependency for the company’s AI strategy
Serve as the technical authority for data architecture decisions during the team’s formative period: technology selection, data modeling patterns, pipeline design, and the foundational choices the rest of the team will build on
Work across the company to understand data needs from engineering, programs, manufacturing, and business operations, and translate those into a platform that serves all of them