Strategic Vision & Roadmaps: Develop and own the product strategy and multi-year roadmap for the Data Foundation program, ensuring it aligns with PVS's overall platform modernization goals. Clearly define phased milestones and outcomes, communicating a compelling vision to stakeholders and team members.
Data Platform Leadership: Lead the Data Foundation program to modernize PVS's data architecture and infrastructure. This includes driving the implementation of a unified, cloud-based data platform (leveraging AWS or similar) that replaces legacy systems, establishes single sources of truth for key data domains, and provides scalable, real-time data access via APIs. Ensure data governance, privacy, and quality standards are built into the platform from the ground up.
Cross-Functional Leadership: Serve as the product lead at the top of the SDLC for this initiative - coordinate and inspire cross-functional teams of software engineers, architects, data scientists, UX/UI designers, and business stakeholders. Without direct managerial authority, exercise influence and thought leadership to drive decision-making, resolve conflicts, and keep everyone aligned on product objectives and timelines.
Product Development Lifecycle Innovation: Introduce and champion the use of generative AI and agent-driven tools in our product development process. For example, explore how AI can automate parts of requirements gathering, coding, testing, or analytics - improving development efficiency and product quality. Investigate opportunities to integrate generative AI into the products themselves (such as AI-driven insights or personalization within the PVS platform) in support of a cutting-edge user experience.
Execution & Delivery: Oversee the end-to-end execution of product initiatives, from concept through launch and iteration. Write clear product requirements (PRDs), define user stories and acceptance criteria in partnership with Agile teams, and maintain a well-groomed backlog for Data Foundation workstreams. Prioritize features by balancing strategic impact, user value, and development effort. Ensure on-time delivery of key roadmap milestones, adjusting plans as needed based on feedback and changing requirements.
Stakeholder Engagement: Collaborate closely with internal stakeholders - including other Product Vertical leaders, Program/Project Managers, and executives - to ensure the Data Foundation program meets the needs of various learning products and operational teams. Communicate program progress, challenges, and next steps through presentations and reports. Act as the voice of the customer and the business , ensuring that delivered capabilities drive tangible improvements in user experience, operational efficiency, and platform capabilities.
Technical Depth & Decision-Making: Use your strong technical background to make informed product decisions. Understand the complexities of cloud data pipelines, database technologies, and modern data architectures well enough to trade off approaches with architects and engineers. Ensure that architectural choices serve the product vision (e.g., selecting appropriate AWS services, designing for scalability and resilience, aligning data models with industry standards like Ed-Fi for education data).
Innovation & Continuous Improvement: Stay abreast of industry trends in data platforms, analytics, and AI/ML . Identify opportunities to incorporate new technologies or practices (for example, event-driven data architectures, graph databases for relationship data, or advanced analytics/ML on our educational data). Foster a culture of innovation and experimentation within the team - for instance, pilot an "AI agent" that can assist with monitoring data quality - and scale successful experiments into full features.
Measurement & Accountability: Define key performance indicators (KPIs) and success metrics for the Data Foundation program. For example, metrics might include data availability/reliability, performance improvements, or adoption rates of new data APIs by other teams. Track these outcomes and use data to inform product decisions and demonstrate program value.
Mentoring and Leadership (Individual Contributor Role): Although this role does not have direct reports, you are expected to mentor and guide junior product managers and product owners within the team, sharing your expertise across the product management organization. Act as an expert consultant for other product teams in your areas of ownership. Help foster best practices in product management, especially in areas like technical product management and AI adoption within the SDLC.
Experience: 10+ years of product management experience, with a significant portion in platform or technical product management roles. Proven track record driving large-scale initiatives end-to-end - ideally including data platform or infrastructure projects. (Experience with identity and access management products is a plus but not required.) Experience should demonstrate increasing responsibility and leadership influence (principal or lead PM level).
Domain Expertise: Strong knowledge of data architecture and management . Familiarity with databases (relational and NoSQL), data warehousing or lakehouse concepts, ETL/ELT pipelines, and data governance best practices. Understanding of data privacy regulations and practices for handling sensitive information is important (education data experience is a plus).
Technical Acumen: Comfortable discussing and understanding complex technical designs with architects and engineers. Hands-on experience with cloud platforms (AWS preferred) and modern software architecture (microservices, APIs, event-driven systems). Ability to grasp AI/ML concepts and their application in products - you don't need to be a data scientist, but you should understand how AI technologies (e.g., large language models, recommendation systems) can be applied to solve product problems.
Leadership & Collaboration: Exceptional cross-functional leadership skills. Able to lead by influence and inspire teams that do not report to you directly. Excellent communication and stakeholder management abilities - can distill complex ideas into clear proposals and status updates for both technical teams and executive leadership. Experience working in an Agile/Scrum development environment, collaborating closely with engineering on user story definition, backlog prioritization, and sprint planning.
Innovation Mindset: Demonstrated interest or experience in cutting-edge technologies , especially **generative AI,
