Data Product Manager
New
United StatesFull-TimeManager
Salary95,800 - 124,500 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSSQLSnowflakeAzureData visualizationDatabricks
Requirements
- 7+ years of experience in data product management, analytics, product management, or data-driven solution delivery.
- Bachelor’s degree in a quantitative or technical field (engineering, computer science, mathematics, economics, or equivalent experience).
- Strong proficiency in SQL and experience working with large, complex datasets.
- Hands-on experience with modern data platforms such as Snowflake, Databricks, AWS, or Azure.
- Strong understanding of data governance, metadata management, and data lifecycle principles.
- Proven ability to translate data into actionable insights and influence strategic business decisions.
- Experience working with BI tools, data pipelines, and data visualization platforms.
- Strong communication and storytelling skills, with the ability to simplify complex data concepts for diverse audiences.
- Ability to manage ambiguity, prioritize effectively, and deliver in fast-paced environments.
- Experience collaborating across engineering, analytics, product, and business teams.
Responsibilities
- Define and drive the vision, strategy, and roadmap for data products across assigned domains, ensuring alignment with business objectives and user needs.
- Translate complex business requirements into structured data product specifications, including data definitions, quality rules, and functional requirements.
- Collaborate with data engineering and data science teams to design, build, and enhance scalable data products and analytics solutions.
- Establish and maintain data governance, metadata management, and quality standards to ensure accuracy, consistency, and compliance.
- Develop and manage product artifacts such as epics, user stories, data flow diagrams, and entity-relationship models.
- Analyze large datasets using SQL and other tools to generate insights and support strategic decision-making.
- Define and track KPIs such as data quality metrics, usage, adoption, and product performance.
- Drive adoption and value realization of data products through training, enablement, and stakeholder engagement.
- Partner cross-functionally to identify opportunities for new data products and continuous improvement of existing assets.
- Support testing, validation, and lifecycle management of data products from development through production.
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