Lead Data Product Manager

Workiva

Lead Data Product Manager role at Workiva, focusing on data product development and stakeholder collaboration. Responsible for roadmap definition, product lifecycle, and cross-functional partnerships.

Posted 7/11/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $151,000 - $242,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudETLPandasPythonSQL

About the role

Key responsibilities & impact

Define and Own the Data Product Roadmap
Partner with Finance and P&C leaders to identify data gaps, prioritize opportunities, and build a roadmap that delivers measurable value
Design data products that support multiple consumption patterns, including BI tools, AI-enabled experiences, and business applications
Use data usage patterns — dashboard activity, query volume, and recurring reporting requests — to proactively identify unmet needs
Translate business objectives into clear requirements, challenging assumptions early to ensure teams are solving the right problems
Serve as a strategic data partner across business stakeholders, Analytics, and Data Engineering teams
Facilitate collaboration across functions while identifying shared data needs and reducing siloed solutions
Build prioritization frameworks that balance competing needs and provide transparency around trade-offs
Communicate effectively with technical teams, business leaders, and executives
Own the full product lifecycle across multiple Finance and P&C initiatives, from discovery through Alpha, Beta, and GA releases
Design user-focused data experiences that balance technical requirements with stakeholder needs
Define and maintain semantic contracts, including field definitions, metric logic, and data grain specifications
Identify reusable data assets and scalable models that support multiple downstream use cases
Maintain a high bar for accuracy, freshness, and reliability while proactively resolving issues before they impact users or AI workflows
Use AI tooling to improve product workflows, including requirements synthesis, AI-assisted SQL validation, catalog generation, and data pattern analysis
Define success metrics for each data product and measure adoption and business impact over time
Develop a deep understanding of end-user workflows and leverage personas to create solutions that address real business needs
Gather stakeholder feedback after launch and continuously improve products based on usage and outcomes
Ensure data products are structured and documented to support trusted AI-enabled experiences
Serve as the product counterpart to Data and Analytics Engineering — owning the what and why while partnering on the how
Collaborate on semantic layer design and dbt transformations to create consistent metric definitions across BI tools, LLMs, reverse ETL pipelines, and applications
Identify opportunities to improve ingestion, transformation, and delivery processes while advocating for investments that increase reliability and scalability
Lead Agile delivery cycles by translating roadmap priorities into Initiatives and Epics with clear acceptance criteria
Lead data catalog efforts for your product area, including discoverability, lineage tracking, and documentation
Champion data quality frameworks and governance standards around access controls, retention, and responsible data usage
Partner across teams to solve systemic data challenges rather than shifting complexity downstream
Present compelling data stories that help technical and non-technical stakeholders make informed decisions
Develop enablement resources, including workshops, documentation, and training, to increase adoption of data products
Build AI literacy across stakeholder communities by helping teams understand how to effectively use AI tools grounded in trusted data
Contribute reusable components, patterns, and best practices that help the broader Data & Analytics organization accelerate delivery

Requirements

What you’ll need

6+ years in product management, with 3+ years focused on data products, analytics platforms, or data infrastructure
Demonstrated experience building data products in a Finance, HR, Legal, operations, or similar business domain
Bachelor’s degree in Engineering, Computer Science, Data Science, Business, or equivalent experience
Proven ability to operate in greenfield or early-stage data environments where you’ve created structure while delivering results
Strong understanding of data pipelines, dimensional modeling, semantic layers, and modern analytics stacks (dbt, Snowflake, BI platforms)
Solid understanding of data governance principles, including metadata management, lineage, quality frameworks, and access controls
Proficiency in SQL with the ability to independently query and validate data
Exceptional stakeholder management skills across senior business and technical audiences
Background in SaaS or product-led growth companies
Experience with data catalog platforms (e.g., Atlan, Alation)
Familiarity with cloud and data tooling: Cloud: AWS, Snowflake; Transformation & orchestration: dbt, Fivetran, Airflow; Visualization: Quicksight, Omni, Sigma; Analytics: SageMaker, Python (Pandas, Seaborn); Project Management: Jira, Confluence, Figma

Benefits

Comp & perks

A discretionary bonus typically paid annually
Restricted Stock Units granted at time of hire
401(k) match and comprehensive employee benefits package

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Data PipelinesDimensional ModelingSemantic LayersData Quality FrameworksData CatalogingAnalytics PlatformsAI ToolingRequirements SynthesisMetric DefinitionsData Pattern Analysis

Soft Skills

Effective CommunicationCollaborationProblem SolvingStakeholder EngagementFacilitation