Sr Product Manager

Location: India

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, Media Ops, 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…