Senior Data Product Manager

Charles Schwab Inc.•Austin, TX

1d•$150,000 - $160,000•Onsite

About The Position

Schwab Technology Services enables the future of how clients manage their money by delivering innovative, reliable technology products that support our mission to democratize access to investing and financial planning. As a Senior Data Product Manager, you’ll sit at the intersection of business strategy, data platforms, and analytics—owning high‑impact data products from concept through adoption and value realization. This is a hands-on, individual contributor role ideal for a product leader who thrives in ambiguity, balances strategy with execution, and is passionate about turning data into measurable business outcomes.

Requirements

Ability to describe one or more data products you owned, including the problem statement and approach followed to deliver the data products.
5+ years of professional experience, including 3+ years of project or product management experience.
Bachelor’s degree.
Experience performing data discovery and writing basic SQL for data validation and understanding.
Demonstrated ability to gather, refine, and prioritize business requirements.
Experience writing user stories and defining acceptance criteria in partnership with business and technology teams.
Demonstrated collaborative leadership style with the ability to lead and influence diverse, cross-functional teams.
Experience defining and tracking product KPIs (e.g., usage and adoption) and communicating product value and outcomes.
Strong communication skills with a high level of comfort leading meetings.
Ability to navigate ambiguity in a fast-paced environment while introducing structure and problem-solving frameworks.
Excellent collaboration skills and comfort working in a matrixed organizational environment.
Active listening skills with the ability to synthesize complex details and dive deep to solve problems.
Self-starter mindset with willingness to learn quickly, make decisions, and pivot toward better solutions when needed.
Flexibility and comfort with change in a transforming organization.

Nice To Haves

Advanced degree in engineering, information systems, or business administration.
Experience with modern data platforms (e.g., Google BigQuery).
Experience defining or implementing data management practices such as metadata, data quality, or data lineage.
Experience building go-to-market or adoption approaches for data products, including user onboarding and awareness.

Responsibilities

Owning the data product strategy and roadmap for one or more data products, grounded in clear business outcomes, value hypotheses, and success metrics (e.g., adoption, data quality, time-to-insight, and business impact).
Translating business needs into well-defined data products, including defining product vision, target users, use cases, data domains, and required capabilities in partnership with business, analytics, and engineering teams.
Leading product discovery and prioritization by facilitating structured working sessions to identify high-value use cases, assess feasibility, and sequence delivery based on impact, dependencies, and organizational readiness.
Acting as the primary Product Manager for data initiatives, developing epics and user stories, defining acceptance criteria, and ensuring alignment between business intent and technical implementation.
Partnering closely with data engineering, platform, and architecture teams to ensure data products are scalable, reusable, well-governed, and aligned with data standards and platform strategy.
Driving execution and delivery discipline by maintaining roadmaps, backlogs, and dependencies in JIRA and Confluence, while actively managing risks, tradeoffs, and stakeholder expectations.
Championing adoption and value realization by working with business partners to enable onboarding, track usage and outcomes, and continuously iterate on products based on feedback and performance insights.
Serving as a subject matter expert for assigned data platforms or domains, helping business teams understand how to leverage data products to advance analytical and operational needs.