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Salary
≈ $22k – $47k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 10+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Sep 24, 2026. First seen by Alion on Aug 25, 2026. Target scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Target is an American general merchandise retailer founded in 1902 as the Dayton Dry Goods Company, which opened the first discount store under the Target name in 1962. It runs roughly two thousand stores across the United States selling apparel, home goods, beauty, electronics, toys and groceries, and has built its position on design partnerships and owned brands that give it products competitors cannot stock. Headquartered in Minneapolis and listed on the New York Stock Exchange, it fulfils the large majority of digital orders from its own stores rather than from separate warehouses.

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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