Senior Technical Product Manager - Data

At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom. Ready to reimagine the DFS industry together?


Do you crave innovation, complex problems, and positive vibes in the fast-paced world of sports? Apply today!

PrizePicks, the leading DFS platform known for accessibility and a thriving community, is seeking a Senior Technical Product Manager - Data to own the full lifecycle of our internal data platform - from data ingestion and ETL pipelines through dimensional modeling, analytics engineering, and the reporting surface that business stakeholders depend on every day. You will be the connective tissue between non-technical business stakeholders and the combined Analytics Engineering and Data Engineering teams that build for them. Your work will define data requirements before features ship, compress UAT cycles, hold committed delivery dates, and make the data platform feel like one product to the people who depend on it.
What you'll do:

Stakeholder Partnership:

• Serve as the product-accountable owner for the internal data platform - from ingestion through the reporting layer - partnering directly with non-analytics business stakeholders: Fraud, RevOps, Partnerships, Acquisition, Player Accounts, Accounting, and Finance.
• Capture requirements at source. Translate raw business problems into product framing, scope, and acceptance criteria before delivery starts.
• Engage upstream product teams - gameplay, discovery, growth - to define data event tracking and instrumentation requirements before features go to build. Prevent reporting gaps by owning the data requirements conversation early in the product development lifecycle.

Strategy:

• Own the product roadmap across the full data platform lifecycle: ingestion, ETL pipeline health, dimensional modeling, semantic layer, and the consumer-facing reporting surface.
• Sequence stakeholder requests and platform investments against capacity and business priorities. Identify recurring patterns and turn them into durable mart and view extensions instead of one-off deliverables.
• Stay ahead of upstream product development - define the dimensional models and data requirements needed downstream before new features ship.

Execute:

• Own the end-to-end product lifecycle for data deliverables: discovery, scope, UAT readiness, delivery, and post-launch adoption.
• Partner with Analytics Engineering on data mart and stable view design. Ensure requirements and UAT criteria are clear before AE begins building, so UAT cycles compress and committed dates hold.
• Partner with Data Engineering on pipeline architecture, ETL design, and data quality standards. Translate business requirements into data engineering specs and own the product decisions around pipeline health, SLAs, and failure triage.
• Own go-to-market and stakeholder communications for new and updated data and reporting capabilities.

Collaborate:

• Bridge analytics, analytics engineering, and data engineering conversations so internal consumers experience one product team, not three.
• Champion a data-quality-first culture - surface data trust issues early and drive them to resolution with the right owners.

Industry Knowledge:

• Develop deep familiarity with how each stakeholder function uses data: what decisions it informs, where the gaps live today, and how reporting needs evolve as the business scales.

Lead:

• Report to the Group Product Manager and manage a combined Analytics Engineering and Data Engineering team. Own the scope and roadmap across approximately 14 engineers and analysts.
• Lead architectural conversations with engineering managers. Translate business strategy into platform investment decisions, not just delivery tasks.

What you have:

Role Specific:

• Strong understanding of data modeling fundamentals: the difference between marts, stable views, and reports - and how each maps to a stakeholder need. Ability to reason about grain, fan-out risk, and dimensional design decisions.
• Working knowledge of ETL and data pipeline concepts: ingestion, transformation layers, data quality validation, and pipeline failure triage. Able to own product decisions around pipeline health without deferring that conversation entirely to engineering.
• Experience engaging upstream product teams on data event tracking and instrumentation requirements before features go to build.
• Track record of translating ambiguous business asks into product scope, acceptance criteria, and UAT-ready specs - across both reporting and data engineering deliverables.

Past Experiences:

• 7+ years of product management experience, with relevant time on internal-facing data products, BI platforms, data engineering, or stakeholder-driven reporting work.
• Experience partnering with or managing data engineering and analytics engineering teams on platform-level deliverables -not just consuming their output.
• Track record of building durable reporting and data infrastructure solutions - ones that became reusable platform assets, not one-offs.
• Comfortable in cross-functional environments where requirements arrive raw and product framing has to be earned across both technical and non-technical stakeholders.

Personal Attributes:

• Excellent communication and interpersonal skills across every level - from Finance executives to data engineers.
• Bias toward listening before scoping. Capable of sitting in the stakeholder's seat before pulling requirements out.
• Comfort with ambiguity and the patience to build trust with stakeholders who have experienced past data quality friction.
• Technical confidence in architectural conversations - able to push back on engineering recommendations with product reasoning, not just defer.

Industry Specific:

• A passion for daily fantasy sports and an understanding of the competitive landscape.

What makes you stand out:

• Hands-on experience with dbt and BigQuery - including model layer reasoning (staging, intermediate, mart) and writing or reviewing dbt tests.
• Experience working in risk, fraud, or finance reporting domains.
• Experience with data quality frameworks such as Great Expectations or dbt tests, and product ownership of data quality SLAs.
• Experience designing or operating an intake and sequencing process for cross-functional data requests.
• A track record of ongoing formal and informal learning in data engineering or analytics engineering.
• Obsessive about sports and/or gaming - maybe you play(ed) yourself.

Where you'll live:

• This is a hybrid position based at our PrizePicks headquarters in Atlanta, GA. #LI-Hybrid

Benefits you'll receive:

In addition to your great compensation package, full-time employees will be eligible for the following perks:

• Company-subsidized medical, dental, & vision plans
• 401(k) plan with company match
• Annual bonus
• Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
• Generous paid leave programs, including 16-week paid parental leave and disability benefits
• Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
• Company-wide in-person events and team outings
• Lifestyle enhancement program
• Company equipment provided (Windows & Mac options)
• Annual performance reviews with opportunities for growth and career development

You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

PrizePicks is an Equal Opportunity Employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.