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Sr Product Manager

Target

Bangalore, Karnataka, IndiaFull-timePosted 2w agoStill listed today

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At a glance

Compensation
No compensation found
Location
Bangalore, Karnataka, India
Schedule
Full-time
Work Authorization
Not specified

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Job overview

The Sr. Data Product Manager will own a portfolio of Target Media data products end‑to‑end, translating technical capability into business outcomes. The role partners with Data Engineering, Analytics and Media teams to build datasets, pipelines, and governance structures, driving measurement and attribution across marketing channels and campaigns.

Skills & qualifications

RequiredNice to have

Skills

Data EngineeringAnalyticsGCPBigQueryPowerBIMedallion ArchitectureDimensional ModellingData GovernanceMarketing MeasurementAttributionMTAMMM

Qualifications

10+ Years Product Management

Full job description

About the role 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. What you’ll own 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.

Analytics & Data Fluency — Must Have Strong working knowledge of analytics and data concepts, with the ability to partner effectively with Analytics teams. You should be comfortable with:

  • Customer-level and aggregated analytics
  • Data aggregation and dimensional modelling
  • Data pipelines and data transformations
  • Data quality and reconciliation
  • Metric definitions and semantic layers

Building data products with Data Engineering 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.

Platform & ecosystem stewardship Solid understand of Data Platform, cloud environment of GCP/BQ/PowerBI

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.

What you’ll bring

  • 10+ years in product management, with meaningful time across Measurement, data products, data platforms, AdTech/MarTech, or retail media.
  • Proven understanding Marketing Measurement concepts of Attribution, MTA, MMM, etc.
  • 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.
  • Track record of translating technical capability into measurable business outcomes and influencing cross-functional partners without direct authority.
  • Strong understand if PM Practices and working with Data Engineering, Analysts and Stakeholders.

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