We are hiring a Sr. Data Product Manager to own a portfolio of Target Media data products end-to-end — from business definitions and contracts through datasets, pipelines, quality, governance, and delivery.
This is a hands-on role that works closely with Data Engineering, Analytics and Media Partners. You will partner with DE day-to-day to build and evolve datasets and pipelines across a medallion (Bronze/Silver/Gold) architecture, shape schemas and contracts, and make sure the data products that result are trusted and well-governed. The core job is to translate technical capability into business outcomes. You will sit at the intersection of Roundel product, Data Engineering, MediaOps, and the broader Media Data Consumers.
We are looking for a Sr. Product Manager — Measurement & Analytics to lead the strategy, development, and evolution of data products that enable marketing measurement and attribution across Target Media.
Measurement – Must Have
This role sits at the intersection of Product, Marketing Measurement, Analytics and Data Engineering. You will own products that help the organization understand the impact of marketing investments across channels, campaigns, audiences, and customer journeys.
The initial focus will be on Roundel and Target marketing spend, with the opportunity to expand measurement capabilities across loyalty, promotions, and other enterprise marketing investments.
A core responsibility will be building and scaling attribution and measurement capabilities that connect marketing exposure and spend to business outcomes, enabling stakeholders to understand what worked, why it worked, and where to invest next.
Strong working knowledge of analytics and data concepts, with the ability to partner effectively with Analytics teams. You should be comfortable with:
You work hand-in-hand with Data Engineering to turn requirements into real datasets and pipelines — translating business needs into clear specs, prioritizing the backlog, and making trade-off calls with DE on design and sequencing.
Data governance and data quality are central to this role, not an afterthought. As operational systems and automated workflows act on Roundel data, the cost of a stale or wrong data product rises sharply.