Lead Data Product Mgr

Lead Data Product Mgr

As a Lead Data Product Manager, you will play a critical role in the end-to-end lifecycle of our data products. You'll blend deep product management expertise with genuine technical fluency in modern data platforms — bridging strategic business objectives and the powerful data assets that drive them. You will champion a data-driven culture across diverse stakeholders, from marketing and operations to data scientists and ML engineers, ensuring the data products we build are not only strategically impactful but architecturally sound.

You'll be at the forefront of translating complex business needs into technically precise data product requirements — fluent enough in Snowflake, dbt, and AWS architectures to engage meaningfully with engineers, and sharp enough on business strategy to align executive leadership. This role is for someone who treats data as a product, obsesses over adoption, and drives measurable outcomes at the intersection of business value and modern data engineering.

• 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.

• 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.

• 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.

• 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.

• 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.

• A minimum of 7+ years in Product Management, data product ownership, or Business Analysis, with a strong focus on data products, business intelligence, or data warehousing — ideally within a cloud-native data stack.
• Working knowledge of Snowflake for data warehousing, including an understanding of performance tuning concepts, schema design, and query optimization.
• Familiarity with dbt (Data Build Tool) for SQL-based transformation workflows — including model organization, testing, documentation, and version control practices.
• Understanding of AWS data services (Lambda, Kinesis, S3, Glue) and their role in event-driven, real-time, and serverless data architectures.
• Comfort with Python and/or SQL sufficient to review technical artifacts, evaluate data models, write precise acceptance criteria, and…