# Product Manager, Semantic Layer

**Company**: Anduril Industries
**Location**: Costa Mesa, California
**Experience**: senior
**Job type**: full-time
**Salary**: $166,000-$220,000 USD
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/andurilindustries/jobs/5178177007?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_bb698d09-235

## Description

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology.

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.

## Responsibilities

- 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.

## Required Qualifications

- 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 team speed.

- Demonstrated ability to work across multiple data teams with competing definitions and align them on a shared model.

- Ability to operate with high autonomy while maintaining judgment, communication, and alignment across data engineering, business partners, and leadership.

- Excellent written and verbal communication skills, with the ability to influence senior engineers, data teams, and executive leadership.

- Degree in Computer Science, Information Systems, Engineering, Statistics, or related field, or equivalent practical experience.

- U.S. Person status is required as this position needs to access export controlled data.

## Preferred Qualifications

- Hands-on experience with a semantic layer or metrics layer (dbt Semantic Layer, Cube, LookML, AtScale, Malloy, or similar) and modern warehouses such as Snowflake, BigQuery, or Databricks.

- Experience owning or contributing to a master data management program, including entity resolution, golden records, and stewardship.

- Familiarity with frontier AI tooling, AI coding assistants, and agentic development workflows applied to data and analytics.

- Experience building against a data catalog, lineage, or governance tooling in a large or fast-growing data estate.

- Familiarity with enterprise systems and business process domains such as ERP, CRM, finance, supply chain, or manufacturing.

- Experience in hyper growth startup-like environments, with demonstrated success balancing speed, ambiguity, autonomy, and long-term data health.

- Eligible to obtain and maintain a U.S. Secret security clearance.

## Skills

### Required
- product management
- data engineering
- analytics engineering
- data analytics
- master data management
- semantic layers
- dimensional modeling
- data pipelines

### Nice to have
- semantic layer
- metrics layer
- dbt Semantic Layer
- Cube
- LookML
- AtScale
- Malloy
- Snowflake
- BigQuery
- Databricks
- entity resolution
- golden records
- stewardship
- frontier AI tooling
- AI coding assistants
- agentic development workflows

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