Senior Product Manager - Enterprise Data Products

About this role

The next part of your journey is right around the corner — with The Standard.
A genuine desire to make a difference in the lives of others is the foundation for everything we do. With a customer-first mindset and an intentional focus on building strong teams, we’ve been able to uphold our legacy of financial stability while investing in new, innovative technologies that support the needs of our customers. Our high-performance culture focused on operational excellence thrives thanks to remarkable people united by compassion and a customer-first commitment. Are you ready to make a difference?
Job Summary

At The Standard, Agile Product Managers connect customer needs, business priorities, and technology to deliver measurable value. Within Enterprise Data, the Senior Product Manager turns enterprise data into reliable products for internal business teams, analysts, operational systems, digital experiences, AI solutions, partners, and other consumers. Following a data-as-a-product approach, teams deliver analytical and transactional data products across the data lifecycle.

The Sr. Product Manager owns strategy, roadmap, prioritization, lifecycle, adoption, and business outcomes for an enterprise data-product portfolio.

Working with customers and stakeholders, this role translates data needs for decisions, experiences, processes, and downstream products into defined products with measurable outcomes. Partners include Data Engineering, Architecture, Data Governance, Analytics, AI, and business teams.

Success means customers can find, understand, trust, access, and use data that measurably improves processes, experiences, and decisions.

Key Responsibilities:

Data Product Strategy & Roadmap

• Own portfolio vision, strategy, lifecycle, and a forward-looking roadmap explaining direction and rationale.

• Translate customer problems, enterprise strategy, regulatory requirements, and technology opportunities into priorities.

• Define each product’s customers, problem, value proposition, and outcomes.

• Prioritize investments and resolve competing needs using customer value, reuse, strategic alignment, risk, effort, and measurable outcomes.

• Balance new development with enhancements, technical debt, quality, modernization, and lifecycle needs.
Customer Discovery & Product Definition

• Understand internal and external customers’ workflows, decisions, experiences, and data needs.

• Continuously engage business, operations, analytics, application, digital-product, and partner stakeholders in discovery.

• Translate customer problems into capabilities, requirements, outcomes, and success measures.

• Distinguish one-time requests from reusable capabilities serving multiple customers and use cases.

• Define how customers discover, access, understand, and consume products.

• Validate assumptions early through feedback, prototypes, data analysis, and incremental delivery.

• Represent customers in development and lifecycle decisions.
Build Enterprise Data Products

• Partner with Data Engineering and Architecture to build scalable products that meet customer and business needs.

• Guide products from source through transformation, modeling, quality validation, governance, and delivery.

• Select fit-for-purpose delivery with engineering: datasets, APIs, events, files, analytical models, or other data services.

• Define product scope, ownership, service expectations, and consumption patterns.

• Design for reuse rather than customer-specific pipelines, extracts, or point-to-point integrations.

• Address reliability, scalability, performance, and maintainability throughout the lifecycle.
Internal & External Customer Products

• Build data products for internal processes, analytics, reporting, digital experiences, operational systems, and AI.

• Understand data needs for experiences serving employers, employees, partners, and other external consumers.

• Partner with digital and application teams to power customer-facing experiences.

• Define downstream interfaces and service levels; prioritize experience, timeliness, accuracy, reliability, and usability.
Data Quality, Governance & Trust

• Make data quality and trust core product requirements.

• Partner with Governance, Security, Privacy, Risk, and Architecture to meet enterprise requirements and manage sensitive and regulated data throughout the lifecycle.

• Establish business definitions, metadata, lineage, ownership, quality expectations, and access controls.

• Define meaningful quality measures and thresholds with customers and engineering.

• Embed governance in the product experience so customers can confidently understand and use data.
Product Adoption & Value Realization

• Own portfolio adoption and value realization, with measures tailored to each product’s purpose and customers.

• Track adoption, usage, quality, reliability, customer experience, and business outcomes.

• Regularly validate that products solve intended customer problems.

• Use telemetry, feedback, operational performance, and outcomes to guide the roadmap.

• Improve, consolidate, replace, or retire underused and duplicative products.

• Communicate outcomes and portfolio health to business and technology leaders.
Cross-Functional Product Leadership

• Partner across Data Engineering, Data Platform Engineering, Architecture, Governance, Analytics, AI and application Product Management, and business teams.

• Lead complex products across systems, business areas, and delivery teams.

• Translate business problems into product decisions without unnecessarily prescribing technical solutions.

• Explain technical topics to business and executive audiences and align teams on outcomes.

• Resolve conflicts among customer needs, architecture, engineering constraints, governance, and enterprise priorities.

• Mentor Product Managers and Product Owners and mature Enterprise Data product-management practices.

Skills and Background You'll Need

Education: A High school…