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.
What You'll Do:
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.
Required Qualifications & Skills:
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 engage substantively with engineering teams.
Exposure to AI/ML workflows, including feature engineering, training data preparation, and model readiness concepts.
Data Modeling & Architecture: Practical understanding of Kimball's dimensional modeling methodologies and Domain-Driven Design (DDD) concepts — enough to evaluate architectural trade-offs, review data model designs, and drive product decisions in partnership with engineering and architecture teams.
Product Mindset: A genuine passion for treating data as a product — defining data contracts, tracking adoption and quality metrics, and delivering measurable business value through trustworthy, well-documented data assets.
Communication & Influence: Superior verbal and written communication skills, with a proven ability to translate complex technical concepts for executive stakeholders and convert business requirements into precise technical specifications for engineering teams.
Required Education:
Bachelor's or Master's degree in Business, Computer Science, Information Systems, Data Science, or a related quantitative field.
The hiring range for this position in Florida is $148,300.00-$198,800.00 per year based on a 40 hour work week. The amount of hours scheduled per week may vary based on business needs. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
