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Director, Product Management - Enterprise Data Platform

Primary Purpose

The Director, Product Management - Data Systems leads product strategy for our enterprise data foundation, including the Snowflake-based platform and supporting data systems that power OmniAnalyst, OmniInsights, and our broader data product ecosystem. Reporting to the Vice President of Data Product Management, this role defines the capabilities, governance, and roadmap needed to deliver scalable, secure, and trusted data products across the enterprise.

As the product leader for the data platform, this individual serves as the primary intake and prioritization point for platform requests, aligns stakeholders across business and technology teams, and ensures the platform delivers business value while maintaining long-term scalability, performance, and cost efficiency.

Essential Responsibilities

Define Platform Capabilities

+ Defines the capabilities required of the enterprise data foundation, including governed data sharing, access controls, entity resolution, semantic models, shared definitions, and data quality standards.

+ Partners with Data Architecture and Data Engineering to design scalable, secure, and well-governed platform capabilities aligned with enterprise standards.

+ Champions modern data architecture practices, including data mesh and layered (medallion) architectures, to drive consistency and reusability across the enterprise.

+ Establishes standards for semantic context, metadata, governance, and data design to ensure data is consistently understood by business users, analytics tools, and AI applications.

Set Vision & Roadmap for Data Systems

+ Defines and owns the vision and multi-year roadmap for the enterprise data foundation, beginning with the Snowflake platform and evolving as business needs grow.

+ Partners with Data Architecture leadership, business stakeholders, and internal data consumers to align platform investments with enterprise priorities.

+ Collaborates with product leaders supporting OmniAnalyst, OmniInsights, and other data products to ensure platform capabilities meet downstream product needs.

+ Establishes and monitors platform success measures, including data quality, availability, performance, adoption, operational efficiency, and cost optimization.

+ Applies Product Operating Model principles to platform intake, prioritization, backlog management, and outcome measurement.

Intake, Prioritization & Platform Governance

+ Owns the intake and prioritization process for enterprise data platform initiatives, maintaining a transparent framework for evaluating business requests.

+ Balances business value, urgency, risk, dependencies, effort, and cost when prioritizing platform investments and managing tradeoffs.

+ Maintains a strategic backlog and facilitates stakeholder alignment through recurring prioritization forums and governance discussions.

+ Identifies demand trends and opportunities for reusable platform capabilities that improve efficiency and scalability.

Drive Platform Delivery

+ Partners with Data Engineering, Data Architecture, Governance, and platform administration teams to deliver roadmap commitments and platform enhancements.

+ Translates business requirements into technical product requirements, user stories, and acceptance criteria for data models, pipelines, access patterns, and platform services.

+ Leverages SQL and strong technical fluency to validate delivered solutions and ensure business requirements are met.

+ Partners with Governance and FinOps teams to manage platform consumption, forecast costs, and promote cost-effective platform growth.

+ Ensures the platform supports analytics, data science, and AI workloads through strong data quality, lineage, governance, semantic context, and performance management.

+ Establishes performance metrics, reporting, and documentation standards that support platform adoption, reliability, and continuous improvement.

Cross-Functional Partnership & Stakeholder Engagement

+ Builds alignment across business, operations, technology, governance, security, privacy, and compliance stakeholders.

+ Ensures appropriate platform controls are implemented and maintained, including role-based access, masking policies, data classification, retention, lineage, and auditability.

+ Communicates platform strategy, releases, priorities, and performance through executive reporting, documentation, and stakeholder engagement.

+ Acts as a trusted advisor on data platform capabilities, governance requirements, and strategic opportunities.

+ Maintains awareness of emerging Snowflake capabilities and modern data architecture trends to inform platform strategy.

Qualifications

Education

+ Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related technical field required.

+ Advanced degree a plus.

+ Snowflake certification (e.g., SnowPro Core or SnowPro Advanced Architect) preferred.

Experience

+ Ten (10) or more years of experience in product management, data platform, data engineering, or data architecture roles, including at least five (5) years in technical product management for data platforms.

+ Hands-on experience with Snowflake, either as a technical practitioner or product owner of a Snowflake-based platform.

+ Strong experience with modern data architecture concepts including data warehousing, data mesh, dimensional modeling, layered (medallion) architecture, semantic modeling, and ELT pipelines.

+ Demonstrated success shaping data platform capabilities, influencing engineering direction, and partnering across business and technology teams.

+ Experience leading intake and prioritization processes for shared enterprise platforms serving multiple business units.

+ Experience supporting analytics, data science, and AI-enabled use cases preferred.

+ Experience with semantic layers, knowledge graphs, metadata management, entity resolution, master data management, or governed data-sharing solutions preferred.

+ Experience working in regulated industries preferred.

Skills & Knowledge

+ Strong knowledge of enterprise data platform architecture, storage concepts, governance, performance optimization, and cost management.

+ Expertise in data modeling, semantic modeling, metadata management, lineage, and data catalog concepts.

+ Strong SQL ...

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