Why This Role Stands Out
This role offers a fantastic opportunity to shape the future of membership data products, driving strategy and innovation for a reputable company. You'll thrive here if you have extensive experience in data product management and a passion for translating complex data into actionable insights, so don't miss out on this exciting chance to make a significant impact.
Quick Overview
Job Description
Boston, MA – Onsite
Job purpose Data Product Managers decide what gets built and why for membership data products. This role owns the vision, roadmap, prioritization and requirements for trusted data capabilities supporting acquisition, onboarding, engagement, personalization, renewal, retention and lifetime value. It connects membership strategy to reusable products on the Databricks enterprise data platform and enables teams to act on a consistent, governed understanding of the member and membership lifecycle.
Key accountabilities / essential functions
· Defines and maintains the roadmap for membership data products and connects product investments to stated growth, engagement, renewal, retention and member-value objectives.
· Runs discovery with Membership, Marketing, CRM, Personalization, Digital Product, Member Care and Finance users to understand decisions, journeys and operational needs.
· Owns prioritization across member identity and profile, Member 360, acquisition, onboarding, engagement, lifecycle, renewal, retention, offer, audience, attribution and lifetime-value capabilities.
· Defines member and household concepts, lifecycle states, business rules, KPIs, identity requirements, quality thresholds and acceptance criteria in partnership with data governance and domain owners.
· Partners with data, engineering and analytics teams to deliver secure, reusable Databricks data products that can support reporting, activation, experimentation, personalization and AI use cases.
· Ensures privacy, consent, security and responsible-use requirements are designed into member data products from discovery through operation.
· Evaluates opportunities for propensity, churn, next-best-action, segmentation and recommendation capabilities, applying evidence and measurable outcomes before scaling.
· Plans launch, activation, stakeholder readiness, communications, training and adoption measurement across the teams and channels that consume membership data.
· Measures product performance through adoption, trust, time to activation and movement in the membership outcome each product is intended to support.
Scope, impact and decision making
Owns a portfolio of enterprise membership data products serving multiple business and technology functions. Makes roadmap, scope and sequencing decisions within the area and recommends trade-offs involving shared identity, platform, activation and analytics capabilities. Impact is measured through adoption, quality, compliant use, time to value and relevant membership outcomes.
Problem solving and complexity
Membership products require reconciling identity, household, transaction, engagement, channel and service signals while protecting member information. The role must resolve inconsistent definitions, manage cross-channel dependencies and distinguish durable lifecycle capabilities from campaign-specific requests.
Leadership and influence
Leads through product vision, evidence, facilitation and influence rather than direct authority. Creates clarity across business, data, engineering, analytics, architecture, security and change-management partners; makes trade-offs visible; and holds the product team accountable for outcomes and adoption.
Key relationships and communication
· Membership leadership and operations: strategy, lifecycle requirements, KPI definition and prioritization.
· Marketing, CRM and personalization teams: audience, activation, measurement, experimentation and governed use.
· Digital Product, Member Care and Club Operations: member journeys, friction points, service workflows and adoption.
· Data engineering, analytics, data science, architecture, security, privacy and governance: identity, data models, controls, quality and responsible use.
Knowledge, skills and experience
Required qualifications
· 5+ years of relevant product management, data product, analytics product or comparable experience.
· Bachelor's degree in a related field, or equivalent practical experience.
· Experience owning product vision, discovery, roadmap, prioritization, requirements, launch readiness, adoption and outcome measurement.
· Hands-on working knowledge of Databricks and modern lakehouse concepts, including governed data products, pipelines, semantic layers, data quality, lineage and self-service consumption.
· Ability to partner effectively with data engineering, analytics, data science, architecture, security, privacy and business teams.
· Ability to define product outcomes and KPIs, use evidence to make prioritization decisions and communicate complex data topics in business language.
· Working knowledge of agile product delivery, backlog management, dependency planning and change adoption.
· Working knowledge of membership and loyalty business language and strategy, including acquisition, onboarding, engagement, tiers, renewal, retention, churn, offers, householding and lifetime value.
Ways of working
· Keeping member and business value the deciding factor when scope pressure would cut it first.
· Building consensus across functions that have no reporting line to each other.
· Changing direction when evidence shows an approach is not working rather than continuing because of sunk cost.
· Holding commitments across concurrent workstreams and communicating early when constraints require a trade-off.
· Holding data products to ethical, privacy, security, quality and governance standards under pressure to ship.
Preferred qualifications
· Retail, wholesale club, subscription, loyalty, financial services or digital commerce experience.
· Experience with customer or member data platforms, identity resolution, CRM activation, personalization or marketing measurement.
· Experience developing governed predictive or AI capabilities for member lifecycle decisions.
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