Lead Data Product Manager

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $151k - $242k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

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Workiva

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2008

☁️ SaaS

💸 Finance

📋 Compliance

SaaS • Finance • Compliance

Workiva is a cloud-based, AI-powered platform that unifies financial reporting, risk and compliance management, and sustainability reporting. It enables teams to connect data across systems, collaborate in real time, automate reporting (including XBRL tagging), and maintain auditability and traceability for regulated disclosures and assurance processes. Workiva serves large enterprises and finance, legal, audit, and sustainability teams to accelerate reporting, improve accuracy, and reduce risk.

📋 Description

• 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

• 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

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