Household lifecycle definitions and formats — acquisition, retention, reactivation and new-to-category — agreed with Analytics and Finance and published as certified data products.
Household attributes and taxonomy — life stage, composition, demographics, interests and purchase behaviour, including third-party enrichment — with clear ownership, lineage and refresh cadence.
Household-level enterprise KPIs such as household-based conversion, taken from definition through UAT, sign-off and production.
Household Applications – Must Have
You will help turn the foundation into applications that change decisions across the enterprise, delivering against a roadmap shaped with your manager and business partners:
Media — map household cohorts to the campaigns they were exposed to, starting with diagnostics and extending to Roundel (retail media) audiences and measurement.
Promotions — connect households to promotion performance so promo teams can see who a promotion actually reached and moved.
Merchandising — household views of newness-led acquisition and assortment relevancy by life stage.
Enterprise growth — an acquisition and reactivation waterfall across drivers (marketing including Roundel, promotions, brand marketing, newness and assortment, new stores), plus who the acquired households are.
Analytics & Data Fluency — Must Have
Strong working knowledge of analytics and data concepts, with the ability to partner effectively with Analytics and Data Science teams. You should be comfortable with:
Household and guest-level analytics, cohorts and segmentation
Entity resolution and identity concepts (guest-to-household mapping, match rates, stability)
Data aggregation and dimensional modelling
Data quality, reconciliation and metric definitions in a semantic layer
Privacy, consent and responsible use of guest and household data
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 and data contracts, prioritising the backlog, and making trade-off calls with DE on design, cost and sequencing.
Platform & ecosystem stewardship
Solid understanding of our data platform and cloud environment (GCP, BigQuery, Power BI). You make household data easy to discover, access and consume — through the certified gold layer, documented data products and self-serve paths for analysts and downstream platforms.
Trust, governance & data quality — critical
Data governance and data quality are central to this role, not an afterthought. Household data drives audience targeting, measurement and executive reporting, so an unstable household ID or a wrong definition ripples across every team that uses it. You own certification, quality monitoring, access and consent controls for the household product line.
Stakeholder partnership
You will work with partners across Marketing, Roundel, Loyalty, Promotions and Merchandising to gather requirements, clarify competing asks, and communicate progress, trade-offs and impact clearly to stakeholders and leadership.
What you’ll bring
3–5 years in product management (or 5+ years in closely related data, analytics or engineering roles with product exposure), with experience on customer or household data products, CDP / customer 360, identity, data platforms, or AdTech/MarTech and retail media.
Strong understanding of customer and household analytics — lifecycle (acquisition, retention, reactivation), segmentation, cohorts and life-stage modelling.
Familiarity with identity resolution and householding concepts, and with how household data powers audiences, measurement and personalisation.
Hands-on experience working closely with Data Engineering to build datasets and pipelines — writing specs, shaping schemas and contracts, and prioritising 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, privacy and consent — treated as critical, not optional.
Experience translating technical capability into measurable business outcomes and working effectively with cross-functional partners without direct authority.
Strong product management practices and excellent communication with Data Engineering, Analysts, Data Scientists and business stakeholders.
Nice to have: exposure to marketing measurement (attribution, MTA, MMM), promotions or merchandising analytics, or third-party data enrichment.
Know More About Us here
Life at Target: https://india.target.com/
Benefits: https://india.target.com/life-at-target/workplace/benefits
Culture: https://india.target.com/life-at-target/belonging
Target Tech: https://tech.target.com/
