Lead Data Product Mgr

Data Product Strategy & Roadmap

Define and own the strategic vision and roadmap for data products across multiple business domains. Translate high-level business problems into technically grounded data product requirements rooted in consumption-ready data architectures, Kimball-inspired dimensional modeling concepts, and Domain-Driven Design (DDD) bounded contexts. Align roadmap priorities with overall company objectives and communicate strategic direction to stakeholders at all levels.

Technical Requirements & Product Definition

• Lead requirements discovery and translate business needs into precise, engineer-ready specifications — including data contracts, semantic layer definitions, dbt model expectations, Snowflake schema patterns, and SQL transformation logic.
• Author acceptance criteria that bridge business intent and technical implementation, enabling Data Product Engineers to build directly from well-defined product artifacts.

Engineering Partnership & Architecture Alignment

• Partner directly with Data Product Engineers and Data Architects to bridge business intent and technical execution.
• Engage substantively in conversations about Snowflake performance tuning, dbt project structure, AWS Lambda and Kinesis pipeline architecture, and data modeling trade-offs.
• Serve as the primary liaison between business teams and engineering, ensuring requirements are clearly understood and data products are built to spec.

AI/ML Data Enablement

• Collaborate with AI/ML and data science teams to define data product requirements that support machine learning initiatives — including feature engineering pipelines, training data preparation, and inference data delivery patterns.
• Ensure data products are designed for ML readiness from the start, partnering with the AI/ML workbench to validate data quality, schema stability, and downstream model compatibility.

Governance, Quality & Operational Excellence

• Define and enforce data governance standards, metadata management practices, data lineage tracking, and data observability frameworks across the data product portfolio.
• Partner with Data Governance and Architecture teams to ensure all data products meet quality, security, and compliance requirements.
• Define KPIs and success metrics, monitor product performance, and use feedback to guide continuous iteration.