Lead Product Manager – Marketing Attribution & Performance

UKG•,

23h•$133,900 - $190,750

About The Position

The Lead Product Manager, Marketing Attribution & Performance owns the product vision, strategy, roadmap, OKRs, and measurable outcomes for Marketing Attribution and Performance at UKG. The role defines how UKG measures marketing-sourced and marketing-influenced impact through first-touch, last-touch, multi-touch, incrementality, and Media Mix Modeling approaches, and helps leaders understand performance, measurement limitations, and investment opportunities. Working across Marketing, Marketing Operations, Sales, Finance and FP&A, Enterprise Data and Analytics, Data Science, Technology, Privacy, and Architecture, the role translates business and analytical needs into product plans and delivers trusted measurement products. It establishes measurement frameworks, attribution rules, KPI definitions, identity and data requirements, reporting, governance, and responsible AI-supported analysis. Success includes a governed view of marketing performance, improved attribution and data quality, greater stakeholder confidence, reduced manual reporting, and measurable business value.

Requirements

8+ years of Product Management experience, preferably with enterprise data, analytics, Marketing Technology, or B2B SaaS products.
Deep understanding of Marketing business processes, campaign and demand-generation operations, performance management, and the relationship between Marketing activity and the sales and opportunity funnel.
Demonstrated experience with first-touch, last-touch, and multi-touch attribution; incrementality; experimentation; Media Mix Modeling; and marketing-sourced versus marketing-influenced reporting.
Strong technical product fluency in data modeling, identity resolution, event and campaign data, integrations, metadata, data quality, lineage, semantic definitions, and analytical architecture.
Working knowledge of anonymous, digital, and known identity patterns across advertising technology, marketing technology, customer data platforms, marketing automation, web analytics, CRM, and Sales Technology.
Proven ability to create a product vision and OKRs and translate them into roadmaps, epics, features, user stories, acceptance criteria, and prioritized backlogs.
Experience partnering with Data and Analytics, Data Science, Engineering, and Architecture teams to deliver trusted reporting and measurement products.
Ability to evaluate and apply industry standards and best practices rather than simply automate current processes.
Strong executive presence, facilitation, storytelling, and influence across Marketing, Sales, Finance, Data, and Technology.

Nice To Haves

Experience with Adobe Workfront or comparable planning and workflow platforms.
Experience with generative or agentic AI in analytics, decision support, or performance optimization.
Experience defining identity, attribution, or measurement products across complex marketing and sales technology ecosystems.
Experience with privacy-conscious data governance, consent-aware measurement, or data quality and observability practices.
Experience applying external standards and best practices in marketing attribution, performance management, experimentation, or Media Mix Modeling.

Responsibilities

Own product vision, strategy, roadmap, OKRs, and measurable outcomes for Marketing Attribution and Performance.
Translate business and analytical needs into prioritized epics, features, user stories, acceptance criteria, and release plans.
Lead discovery, MVP definition, sequencing, dependency management, delivery, adoption, and continuous improvement.
Build a capability roadmap spanning measurement, identity, data products, reporting, experimentation, and decision intelligence.
Define the enterprise measurement framework for marketing-sourced and marketing-influenced pipeline, opportunities, bookings, revenue, and return on investment.
Develop and govern first-touch, last-touch, and multi-touch attribution models with clear business rules, assumptions, limitations, and explanations.
Partner with Data Science and Analytics on incrementality testing, holdouts, lift methodologies, experimentation, and Media Mix Modeling.
Connect campaign, content, channel, audience, spend, engagement, MQL, BDR engagement, opportunity, pipeline, bookings, and revenue data.
Define KPI hierarchies, semantic definitions, attribution windows, opportunity-association logic, source rules, and performance benchmarks.
Deliver reporting and analytical products that explain performance by program, campaign, channel, audience, segment, geography, funnel stage, and investment.
Define product requirements for identity resolution across anonymous IDs, cookies, device and digital IDs, hashed identifiers, known person and account IDs, CRM records, and opportunity entities.
Partner with Data and Analytics and Architecture on canonical data models, identity-stitching rules, data lineage, quality thresholds, reconciliation, observability, and governed data products.
Ensure campaign source, channel, content, audience, cost, program, and other critical metadata are captured and retained through the customer and revenue journey.
Balance measurement goals with privacy, consent, security, platform constraints, data latency, and maintainability.
Identify responsible uses of generative and agentic AI to automate analysis, surface anomalies, explain performance, recommend investments, and improve decision velocity.
Establish human oversight, governance, explainability, and controls for AI-supported analysis and recommendations.
Drive adoption through enablement, documentation, governance, feedback loops, and clear accountability for data and process quality.
Communicate product strategy, measurement approaches, roadmap decisions, model limitations, risks, and outcomes to executives, marketers, analysts, engineers, and sales leaders.