Sr Product Manager

Role Overview

Measurement and Attribution

• Lead the strategy, development, and evolution of data products for marketing measurement and attribution.
• Build capabilities that connect marketing exposure and spend to outcomes across channels, campaigns, audiences, and customer journeys.
• Initial focus is on Roundel and Target marketing spend, with potential expansion to loyalty, promotions, and other enterprise investments.

Data Product Development

• Partner with Data Engineering to build and evolve datasets and pipelines across Bronze, Silver, and Gold architecture layers.
• Translate business needs into specifications, shape schemas and contracts, prioritize technical backlogs, and make design and sequencing trade-offs.
• Own data aggregation, dimensional modeling, transformations, metric definitions, and semantic layers.

Governance and Quality

• Treat data governance and quality as critical requirements for operational systems and automated workflows.
• Oversee data quality, reconciliation, lineage, monitoring, access, certification, and consent.

Qualifications

• At least 10 years of product management experience.
• Experience in marketing measurement, data products, data platforms, AdTech, MarTech, or retail media.
• Understanding of attribution, multi-touch attribution, and marketing mix modeling.
• Hands-on experience with Data Engineering teams building datasets and pipelines, shaping schemas and contracts, and prioritizing technical backlogs.
• Understanding of medallion architecture and modern data platforms, including lineage and cost.
• Strong data governance and data quality knowledge, including certification, monitoring, access, and consent.
• Ability to translate technical capabilities into measurable business outcomes and influence cross-functional partners without direct authority.

Analytics and Platform Skills

• Working knowledge of customer-level and aggregated analytics, dimensional modeling, data transformations, reconciliation, metric definitions, and semantic layers.
• Understanding of GCP, BigQuery, Power BI, and modern data platform architectures such as lakehouse, data mesh, or federated models.