Job Description
About us:
As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's 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 you'll build relationships by being authentic and respectful.
Team overview:
Roundel - Target’s Retail Media Network, runs on data products that power reporting, operational decisioning, and a growing set of analytics, ML, and AI agent use cases.
Roundel’s 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 you’ll 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 team’s definition clashes with another’s (for example, how Roundel defines a lapsed buyer versus how Loyalty does).
Decide what’s 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 what’s 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 don’t 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.
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