Principal Product Manager - Data Platform - Contract

Description:

The Principal Product Manager - Data Platform is responsible for establishing and managing a unified product backlog that advances the enterprise data and analytics platform across data platform operations, platform modernization, analytics and AI, and data architecture functions. This role partners with data product owners, data platform leaders, architects, engineers, analytics teams, governance partners, and business stakeholders to translate strategic needs into prioritized platform capabilities, measurable outcomes, and well-defined delivery increments.
The role is designed as a shared senior product management capability across the data organization. Approximately 70% of the role will focus on data platform product management, product roadmap alignment, technical backlog ownership, value definition, adoption, and stakeholder prioritization. Approximately 30% of the role will focus on scrum master and agile delivery facilitation responsibilities, including sprint planning, dependency coordination, impediment removal, release readiness routines, and delivery transparency.
This position helps unify platform demand across the modern cloud data platform, legacy platform retirement, analytics enablement, semantic and AI-enabled analytics, architecture standards, data quality, metadata, lineage, MDM, governance, security, and operational support needs. The role enables the organization to deliver a coherent, governed, scalable, and customer-adopted data and analytics platform for the bank and association customers.

Responsibilities

• Own and continuously refine the integrated technical product backlog for the enterprise data and analytics platform across data platform, analytics, architecture, modernization, and operational enhancement workstreams.
• Partner with the data owner and delivery leaders to gather, clarify, and prioritize demand related to platform operations, platform transformations, analytics and AI, and data architecture and AI toolkit capabilities.
• Translate cloud data platform strategies (Databricks focused), association customer needs, governance requirements, and technical platform needs into clear epics, features, user stories, acceptance criteria, and measurable outcomes.
• Maintain a multi-quarter product roadmap that balances modernization, operational stability, analytics enablement, architecture standards, MDM, data quality, security, lineage, adoption, cost control, and platform reliability.
• For the technical backlog - drive intake, triage, prioritization, and sequencing of platform work by applying value, risk, urgency, dependency, capacity, customer impact, operational resilience, and strategic alignment criteria.
• Partner with architecture, engineering, operations, analytics, governance, security, and business-facing teams to ensure backlog items are properly scoped, designed, validated, governed, and ready for delivery.
• Define product success metrics including platform adoption, data product consumption, self-service maturity, backlog throughput, delivery predictability, service health, data quality, stakeholder satisfaction, and value realization.
• Support the transition from legacy data platforms toward a unified modern platform by coordinating backlog priorities for migration, data product readiness, business validation, dependency management, and adoption enablement.
• Promote reusable data products, governed semantic models, common metrics, standard architecture patterns, and platform capabilities that reduce duplication and improve consistency across teams and association customers.
• Facilitate agile ceremonies such as backlog refinement, sprint planning, daily standups, sprint reviews, retrospectives, release planning, and cross-team dependency reviews, as appropriate for the delivery model.
• Serve as scrum master for shared or cross-functional delivery efforts by removing impediments, improving team flow, supporting capacity planning, escalating blockers, and promoting disciplined agile execution.
• Create delivery transparency for the SVP, directors, and stakeholders through concise roadmap updates, backlog health views, sprint progress, risks, dependencies, decisions needed, and upcoming milestones.
• Ensure initiatives are production-ready and supportable by coordinating operational readiness, monitoring requirements, support documentation, change management, release readiness, and handoff expectations.
• Partner with business users and association stakeholders to strengthen adoption planning, training needs, feedback loops, validation routines, and communication around platform and analytics capabilities.
• Coordinate with vendor, cloud, infrastructure, and enterprise technology partners when backlog items require external platform features, cost tradeoffs, service dependencies, or vendor roadmap alignment.
• Foster a product operating model mindset across the data organization, with clear ownership, prioritization discipline, measurable outcomes, reusable standards, and continuous improvement.
• Product Management Focus: Approximately 70%
• Own cloud data platform product vision alignment, roadmap governance, backlog prioritization, business value definition, and stakeholder engagement for the unified data and analytics platform.
• Convert strategic objectives and customer needs into actionable epics, features, user stories, acceptance criteria, and success metrics.
• Regularly review backlog health, capacity, sequencing, dependencies, risk posture, adoption readiness, and value realization with director-level stakeholders.
• Ensure product decisions balance speed-to-value with governance, data quality, architecture consistency, platform supportability, security, cost, and regulatory expectations.
• Scrum Master / Delivery Facilitation Focus: Approximately 30%
• Facilitate agile ceremonies and help teams maintain disciplined sprint execution, predictable increments, and a clear definition of ready and definition of done for two scrum teams.
• Track and escalate dependencies, blockers, risks, cross-team decisions, and environment or platform constraints that could affect delivery commitments.
• Support delivery metrics such as sprint predictability, backlog aging, dependency resolution, release readiness, defect trends, and impediment cycle time.
• Coach teams on practical agile behaviors while adapting ceremonies and artifacts to fit enterprise platform delivery, operations, and modernization work.

Job Requirements

Education

• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, Business, or related field required.
• Master's degree, product management certification, agile certification, or equivalent professional experience preferred.

Experience

• 10+ years of experience in product management, data platform delivery, analytics delivery, data engineering, enterprise technology delivery, or related roles.
• 5+ years of experience managing product backlogs, roadmaps, epics, features, user stories, acceptance criteria, prioritization routines, and stakeholder engagement for data or technology platforms.
• Experience working with enterprise data platforms, cloud data ecosystems, lakehouse or warehouse environments, data pipelines, data quality, governance, metadata, lineage, semantic layers, BI platforms, or MDM capabilities.
• Experience supporting agile delivery teams in a scrum master, delivery lead, product owner, or product manager capacity.
• Experience coordinating cross-functional work across data architecture, data platform engineering, analytics, governance, security, infrastructure, operations, and business stakeholders.
• Experience operating in regulated, risk-sensitive, audit-conscious, or financial services environments preferred.
• Experience with modern data and analytics technologies such as Databricks, Azure, Power BI, MicroStrategy, Snowflake, SQL, Python, Spark, orchestration tools, catalog or governance platforms, and data quality tooling preferred.

Skills & Competencies

• Strong product management mindset with ability to define value, prioritize demand, sequence delivery, manage tradeoffs, and communicate roadmap decisions clearly.
• Strong understanding of modern data platforms, analytics platforms, semantic modeling, data quality, governance, MDM, metadata, lineage, access controls, and cloud data architecture concepts.
• Ability to translate complex technical needs into business-oriented product outcomes, delivery increments, acceptance criteria, and executive-ready updates.
• Strong agile delivery facilitation skills, including backlog refinement, sprint planning, dependency management, impediment removal, release coordination, and retrospective-driven improvement.
• Strong stakeholder management skills with the ability to influence without direct authority across directors, architects, engineers, analysts, governance partners, vendors, and business leaders.
• Strong prioritization skills with the ability to balance modernization, operational support, business demand, adoption, security, cost, risk, and technical debt.
• Ability to establish lightweight but disciplined governance routines for intake, backlog hygiene, roadmap updates, delivery metrics, dependency tracking, and decision escalation.
• Working knowledge of SQL, data pipelines, ETL / ELT, analytics consumption patterns, dashboarding, data validation, platform observability, and cloud platform concepts.
• Clear written and verbal communication skills, including the ability to produce concise executive updates, product briefs, release notes, user-facing communications, and decision logs.
• High ownership, organizational discipline, curiosity, and continuous improvement orientation, with the ability to learn emerging data, analytics, and AI capabilities and apply them pragmatically.

Success Measures

• Unified and well-prioritized backlog visible to leadership and delivery teams, with clear ownership, acceptance criteria, dependencies, and decision points.
• Improved alignment across platform operations, transformations, analytics, and architecture teams through shared roadmap governance and consistent prioritization routines.
• Higher delivery predictability, clearer sprint outcomes, reduced unmanaged dependencies, and faster escalation of blockers and risks.
• Improved stakeholder confidence through transparent progress reporting, measurable value, adoption planning, and disciplined release readiness.
• Greater reuse of platform capabilities, data products, semantic models, architecture patterns, and governance standards across the enterprise data and analytics ecosystem.
• Preferred Certifications
• Certified ScrumMaster, Professional Scrum Master, SAFe, PMI-ACP, or comparable agile certification preferred.
• Product management certification, data management certification, cloud platform certification, or analytics platform certification preferred.