Sr Product Manager

About us:

As a Fortune 50 company with more than 400000 team members worldwide Target is an iconic brand and one of Americas leading retailers. Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here we believe your unique perspective is
important and youll build relationships by being authentic and respectful.

Team overview:

Roundel - Targets Retail Media Network runs on data products that power reporting operational decisioning and a growing set of analytics ML and AI agent use cases.

Roundels data products sit underneath reporting campaign operations and a growing set of analytics use cases. This role exists to make that foundation trustworthy and well-governed so the business can rely on a single consistent set of definitions and data products regardless of who is consuming them.

Role overview:

We are hiring a Sr Product Manager to own a portfolio of Roundel 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. 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.

What youll own

The semantic layer business definitions and meaning

You own the business definitions that sit on top of the data. When someone says revenue active campaign or New buyer there should be one agreed meaning behind it not several conflicting versions across dashboards and teams.

• Own the definitions. Align the right people on what each key metric and entity means document it and resolve conflicts when one teams definition clashes with anothers (for example how Roundel defines a lapsed buyer versus how Loyalty does).

• Decide whats official. Determine which metrics and entities are certified and supported versus ad-hoc prevent redundant or overlapping definitions and keep the catalogue from sprawling.

• Set access and usage rules. Define whats exposed to which consumers what freshness and quality each use case needs and what is governed or restricted.

• Protect definitions over time. When a definition or schema changes make sure downstream reports and models that depend on it dont quietly break.

Building data products with Data Engineering

You work hand-in-hand with Data Engineering to turn requirements into real datasets and pipelines. This is a core hands-on part of the role not a hand-off.

• Partner on datasets and pipelines from requirement to production translating business needs into clear specs prioritizing the backlog and making trade-off calls with DE on design and sequencing.

• Apply medallion architecture (Bronze/Silver/Gold) deliberately understanding what belongs in each layer where certified Gold datasets should live and how raw and refined data flow through the stack.

• Own schemas and contracts between producer and consumer teams including the metadata and quality guarantees each data product carries.

• Govern lineage and cost end-to-end with DE traceability from source to consumption plus compute storage and tiering efficiency as volumes grow.

Platform & ecosystem stewardship

• Govern the catalogue so Roundel campaign metrics and related data assets are discoverable documented and linked.

• Define SLAs for freshness availability and quality that reflect reporting operational and downstream consumer needs.

Designing for AI & agents

Reporting and operational systems are the consumers today but ML and AI agents are a fast-growing consumer set. Agents consume data differently from humans they need structured context reliable retrieval and well-defined access rather than a dashboard and you should design data products that can serve them as this adoption grows.

• Design for both humans and agents human-readable views and structured well-defined access over the same governed data product rather than maintaining separate copies.

• Define access and retrieval patterns for ML and agent consumption schemas freshness and the metadata an agent needs to use a data product correctly.

• Evaluate data products against agent use not just accuracy and completeness but whether agents relying on the product can complete their tasks reliably.

Trust governance & data quality critical

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.

• Treat data quality as a risk metric with explicit thresholds and alerts so issues are caught before they affect reporting or operational decisions.

• Drive pre-launch certification for critical events metrics and datasets plus ongoing health monitoring with defined escalation paths.

• Own governance controls access consent retention and policy enforcement in partnership with the data governance function.

• Maintain audit trails and documentation so consumers understand what a data product contains where it comes from and how to use it correctly.

About you:

• 6 years in Product Management with meaningful time on data products data platforms AdTech/MarTech or retail media.

• Hands-on experience working closely with Data Engineering to build datasets and pipelines writing specs shaping schemas and contracts and prioritizing a technical backlog.

• Solid understanding of medallion architecture (Bronze/Silver/Gold) and modern data platforms (Lakehouse data mesh or federated architectures) including lineage and cost.

• Strong grasp of data governance and data quality certification monitoring access and consent treated as critical not optional.

• Working literacy in business definitions and semantics able to define what an entity or metric means and reconcile differences across domains.

• Understanding of how ML systems and AI agents consume data (structured context retrieval access patterns) and how that shapes data product design.

• Track record of translating technical capability into measurable business outcomes and influencing cross-functional partners without direct authority.

Nice to have

• Direct experience in retail media advertising or martech

• Experience defining or operating certified/governed dataset programs.

• Familiarity with semantic layers metric-definition tooling or designing data products for agent and RAG consumption.

Useful Links:

• Life at Target- Experience:

  Senior IC