Product Manager, Semantic Layer

Job description

Job 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.

Job requirements

• 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.