Senior Product Manager II

This position also requires close collaboration with a broad set of stakeholders, including operations and sales teams for project execution, third-party vendors for data partnerships and integrations, and external clients to onboard their datasets, explain methodologies, and interpret results.

What You’ll Do:

Product Strategy & Roadmap Ownership

Own the product strategy and roadmap for our Outcomes Data Processing Pillar, focusing on data ingestion, transformation, and third-party integrations across our outcomes measurement solutions.

Independently manage trade-offs, balancing short-term client delivery needs with long-term infrastructure scalability, data governance, and technical debt reduction.

Monitor industry trends and evolving client needs to keep the product roadmap current, innovative, and competitive.

Technical Execution

Partner with engineering and data science teams to design processes, databases, and pipelines that support campaign advertising performance datasets.

Translate complex business needs and client requirements into clear technical specifications, detailed user stories, and actionable acceptance criteria.

Drive day-to-day product delivery: manage the product backlog, refine user stories, plan sprints, and perform user acceptance testing (UAT).

Regularly collaborate with cross-functional development teams and product owners to design and deliver platform enhancements and new capabilities.

Stakeholder Alignment & Client Enablement

Communicate the product strategy, technical tradeoffs, and results clearly to both technical and non-technical audiences, including executive leadership and external clients.

Work directly with clients to onboard their datasets, explain measurement methodologies, and help interpret project results.

Lead the roll-out of new data capabilities and platform features, including user training, documentation, and support, to ensure successful internal and external adoption.

Build strong, ongoing relationships with internal sales, operations, and customer success teams to understand needs, champion feedback loops, and evolve solutions.

Product & Industry Experience: 5+ years in product management, with a significant focus on data platforms, ad tech, or media analytics solutions (and 8+ years of total relevant experience in the advertising or media industry).

Ad Tech Ecosystem Expertise: A strong understanding of the digital and TV advertising ecosystem, including ad tagging, audience identity data, and ROI measurement, along with awareness of data privacy regulations (e.g. GDPR, CCPA).

Measurement Methodologies: Direct experience with broader advertising measurement methodologies, including marketing mix modeling, multi-touch attribution, or incrementality measurement.

Analytical & Technical Fluency: Strong analytical fluency with hands-on experience querying data warehouses using SQL, and able to understand data architecture, APIs, and platform scalability challenges.

Data Infrastructure & Release: Working knowledge of cloud infrastructure and data orchestration tools and concepts (e.g. AWS, Azure, Airflow) along with familiarity with technical release patterns and versioning.

Stakeholder Communication: Exceptional written and verbal communication skills, with the ability to influence and manage both technical and non-technical stakeholders.

Cross-Functional Collaboration: Demonstrated ability to work cross-functionally with engineering, data science, sales, and operations teams.

Agile Practices: Working knowledge of Agile or similar iterative development methodologies (e.g. Scrum, Kanban) and experience with tools such as Jira and Confluence.

Autonomy & Ownership: A natural problem-solver with a knack for turning complex, ambiguous problems into clear, actionable plans.

Preferred Qualifications

Brand Lift Expertise: Direct experience with panel-based research, survey methodologies, or brand lift outcome measurement.

Data Partnerships & Integrations: Proven background managing third-party data providers end-to-end, from initial discovery through full data onboarding and integration.

Data Governance: Familiarity with data governance frameworks, privacy-enhancing technologies, or identity/data clean room solutions.

Data Science Collaboration: Background in collaborating with data science teams on methodology design and data validation.

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