Product Manager, Semantic Layer

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI‑powered operating system that turns thousands of data streams into a realtime, 3D command and control center.

As the world enters an era of strategic competition, Anduril is committed to bringing cutting‑edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.

Corp Tech Platform is the internal engineering force multiplier behind Anduril's corporate systems.

It drives Corp Tech engineering forward through strategic investment in data platforms, software platforms, QA and release excellence, ERP engineering, and AI infrastructure.

We build the foundations that power both CorpOS, enabling Finance and Growth to operate with speed and precision, and ArsenalOS, the digital backbone of Anduril's hardware enterprise. By unifying systems, accelerating development velocity, and embedding intelligence into every layer, Corp Tech Platform transforms how the company operates end to end.

We are driving Anduril toward becoming an autonomous enterprise. Through initiatives like the Autonomous Software Factory, we are rethinking how software is built, tested, deployed, and evolved by integrating AI into the engineering lifecycle.

Anduril runs on trusted data, and today too much of it means different things to different teams. The Product Manager, Semantic Layer & Ontology owns the product suite that lets us define, build, and maintain a single semantic layer: the shared metrics, dimensions, entities, and relationships that data engineering, analytics engineering, analytics, and master data management all build against. This is a hands‑on role.

You will submit code PRs with AI coding agents to extend the semantic models, ui, and tooling. You will sit at the seam of every data function, so the definitions you ship become the version of the truth the business reports on.

• Own product strategy, roadmap, and success metrics for the product suite that supports building and maintaining the semantic layer and ontology.
• Partner across data engineering, analytics engineering, data analytics, and master data management to define shared metrics, dimensions, entities, and relationships once, then reuse them everywhere.
• Turn conflicting definitions, data disputes, and modeling ambiguity into a governed source of truth that teams adopt.
• Act as a hands‑on builder by submitting code PRs with AI coding agents such as Claude Code for the semantic layer application.
• Build and operate a feedback and triage loop for definition requests, data‑quality issues, and adoption blockers.
• Define governance and contribution standards for the ontology, including ownership, versioning, and change management, so the model stays coherent as sources and teams grow.
• Balance a canonical master data model against the speed analytics and downstream teams need to ship.
• Communicate modeling decisions, trade‑offs, and data‑quality risks to data teams, business stakeholders, and senior leadership.
• Establish a model for AI‑enabled product development where the PM contributes directly to the semantic layer without compromising lineage, reliability, or governance.

• 5+ years of experience across product management and one or more data functions: data engineering, analytics engineering, data analytics, or master data management, in a fast‑paced environment.
• Demonstrated experience owning a data product, or working in a data technical function and pivoting into product.
• Demonstrated builder orientation, including submitting code such as SQL, dbt, Python, or semantic‑layer definitions, and shipping with AI coding agents.
• Strong technical fluency in semantic layers, dimensional modeling, data pipelines, and the trade‑offs between a governed model and local…