Senior Product Manager - Enterprise Data Products

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 diploma or GED is required; Bachelor’s degree preferred.
Experience: 8+ years of progressive experience in product management, data, analytics, technology, or a related business environment. Experience working with a globally dispersed team is highly preferred.
Ownership of complex products through discovery, delivery, adoption, and lifecycle management.
Experience building data, analytics, API, integration, digital, or enterprise technology products.
Experience serving internal customers and supporting external customer experiences.
Experience partnering with Data Engineering, Software Engineering, Architecture, Analytics, or Data Governance.
Experience navigating complex enterprises, multiple stakeholders, and competing priorities.
Product Management Skills
Strong discovery, strategy, roadmap, prioritization, and lifecycle-management skills.
Identify customer problems beyond requirements and distinguish reusable capabilities from one-time requests.
Translate a clear product vision into executable priorities.
Strong customer discovery and stakeholder management.
Define outcomes and measure product success.
Make difficult tradeoffs using customer value, outcomes, risk, strategic alignment, and technical feasibility.
Strong written, verbal, and visual communication.
Data Product Skills
Strong understanding of data-as-a-product principles and modern enterprise data architectures.
Understand ingestion, transformation, modeling, quality, storage, integration, and delivery.
Understand analytical and transactional product patterns, including APIs, events, streaming, data sharing, and batch delivery.
Understand metadata, lineage, business definitions, quality, privacy, security, and access controls.
Familiarity with cloud data platforms and lakehouse architecture.
Working knowledge of Azure Databricks, Delta Lake, Unity Catalog, SQL, APIs, and related technologies preferred.
Discuss architecture and engineering tradeoffs without personally designing or implementing solutions.
Analytics & AI Understanding
Understand how analytics, machine learning, GenAI, and operational applications use enterprise data and how product design affects outcomes.
Identify shared data products serving analytics, AI, operational, and digital use cases.
Apply understanding of emerging Data and AI capabilities to evolving customer needs and roadmap decisions.
Key Behaviors of a Successful Candidate
Customer Focus: Start with customer problems and value, not available data or technology.
Product Thinking: Treat data as a product with customers, outcomes, quality expectations, lifecycle decisions, and measurable value.
Driving Success: Own decisions and outcomes; deliver value across organizational boundaries.
Technical Curiosity: Understand engineering, architecture, analytics, and AI to ask informed questions, challenge assump

Work arrangement
Yes