Sr. Product Owner - GM Motorsports

General Motors•Concord, NC

4d•Hybrid

About The Position

The Senior Product Owner will lead the product strategy, roadmap, and delivery execution for key GM Motorsports data and analytics products that support Strategy, Vehicle Performance, and Competition Engineering - across NASCAR, IndyCar, and other GM racing programs. The role combines product ownership with scrum leadership responsibilities, partnering closely with motorsports stakeholders and leading cross-functional delivery across the data science and data engineering teams. Direct motorsports experience is not required; success in this role depends on the ability to quickly understand stakeholder needs, translate them into roadmaps and backlog items, and drive high-quality, timely delivery in a complex, fast-paced environment.

Requirements

Bachelor’s degree in Engineering, Computer Science, Data/Analytics, Business, or related field; or equivalent experience.
5+ years experience as a product manager, product owner, business analyst, systems analyst, or similar role.
Significant experience in product management / product ownership roles, including: Defining product vision and multi-release roadmaps.
Managing and prioritizing complex backlogs across multiple stakeholder groups.
Delivering digital/data products in an agile environment.
Demonstrated experience serving as a Product Owner and/or scrum lead for one or more agile teams (e.g., data, analytics, software).
Proven ability to work cross-functionally and influence without formal authority, including partnering with technical and non-technical stakeholders at multiple levels.
Strong communication skills with a track record of: Presenting concise, data-informed updates and recommendations to senior leadership.
Translating technical concepts into business-relevant language for non-technical stakeholders.
Providing clear, actionable guidance to development teams.
Familiarity with data science and data engineering concepts (e.g., analytics use cases, data pipelines, model development and deployment, experimentation) sufficient to: Ask the right questions.
Understand trade-offs and constraints.
Make informed prioritization decisions.
Demonstrated ability to operate at a senior scope by: Solving complex, often new problems that require deep experience and strategic thinking.
Leading large or high-visibility initiatives with cross-functional impact.
Mentoring less-experienced colleagues and elevating team practices.

Nice To Haves

Experience working with and owning data-heavy products or platforms in partnership with data science and/or data engineering teams (e.g., performance analytics, telemetry, operations), including defining SLAs, data contracts, and quality expectations for downstream consumers.
Familiarity with modern data and analytics technology stacks, ideally including: Cloud platforms (e.g., Azure).
Data engineering tools (e.g., Databricks, Spark, modern data pipelines, streaming technologies such as Kafka).
Analytics / BI / experimentation and ML platforms.
Solid understanding of modern data engineering concepts and patterns (data lakes/lakehouse, batch and streaming pipelines, warehousing, orchestration, observability), with the ability to discuss trade-offs with engineers.
Ability to read and reason about technical designs (e.g., high-level schemas, pipeline diagrams, architectural decisions) and use that understanding to prioritize work, not to implement code hands-on.
Product ownership experience in an engineering-heavy domain (e.g., automotive, mobility, manufacturing, motorsports, aerospace, or similar), including designing and tracking product KPIs and success metrics for data/analytics products.
Formal agile training or certifications (e.g., CSPO, PSPO, Scrum Master) and experience tailoring agile practices to complex, multi-team environments.
Demonstrated ability to navigate enterprise processes, governance, and risk while maintaining urgency and forward progress.

Responsibilities

Serve as the accountable Product Owner for cross-functional data science and data engineering teams supporting GM Motorsports.
Own and maintain the product vision, roadmap, and prioritized backlog for motorsports analytics and data products, aligning to business strategy and measurable outcomes.
Act as scrum lead for both teams: Facilitate and continuously improve backlog refinement, sprint planning, sprint reviews, and retrospectives.
Ensure clear, well-defined user stories and acceptance criteria.
Remove impediments and protect the teams’ ability to execute.
Partner closely with motorsport Strategy, Vehicle Performance, and Competition Engineering stakeholders to understand business objectives, workflows, and decision-making needs.
Translate complex requirements into clear, implementable features and experiments.
Weigh scope, risk, and downstream impacts to recommend sequencing and trade-offs.
Collaborate with data scientists, data engineers, architects, and product partners to define solutions that are feasible, scalable, and aligned with enterprise data and AI standards.
Drive cross-functional alignment across multiple groups (e.g., motorsport engineering, IT, security, operations) to ensure dependencies are identified and managed.
Communicate product strategy, priorities, risks, and status effectively: Provide concise, insights-driven updates and recommendations to senior and executive leadership.
Provide clear context, rationale, and direction to delivery teams.
Apply a disciplined approach to outcome measurement by defining and tracking product KPIs and adoption metrics, and using data and stakeholder feedback to iterate on roadmap and backlog priorities.
Serve as a thought partner on data and AI capabilities, helping stakeholders understand what is possible with modern data science and data engineering platforms.
Model and reinforce GM Behaviors (Win with Integrity, Commit to Customers, Innovate and Embrace Change, Speak Fearlessly, Move with Urgency, Be Inclusive, Lead as One Team, Own the Outcome) in day-to-day work and team interactions.
Provide informal leadership and mentoring to other product owners, analysts, and team members; contribute to improving product and agile practices across the broader organization.