Note: The job is a remote job and is open to candidates in USA. Project Management Institute (PMI) is seeking a Manager, Marketing Data & Analytics to own Marketing’s business data architecture, metric definitions, and analytical context. The role leads a team of analysts while designing and validating analytical models, governing marketing measurement and predictive analytics, and partnering with engineering and business teams to deliver reliable insights.
Responsibilities
- Own Marketing's canonical metric definitions and the business logic the governed semantic layer implements, and arbitrate those definitions when marketing, finance, and reporting platforms disagree
- Specify the semantic models for the most complex and cross-domain marketing use cases, and validate what the Snowflake team builds against that specification
- Own the business data architecture for Marketing, defining the facts, dimensions, grain, relationships, hierarchies, and conformed dimensions that connect marketing, customer, product, transaction, and engagement data
- Design analytical models that answer cross-domain questions spanning acquisition, engagement, conversion, certification, membership, renewal, and revenue
- Write advanced SQL to profile data, validate sources, reconcile metrics, build reusable datasets, and investigate complex customer and business questions
- Determine what unstructured content, such as business documentation, definitions, and prior analyses, must be retrievable alongside structured metrics for natural-language and agent-based analysis, and maintain source-to-metric mappings and lineage
- Own the business questions, expected results, and synonym glossary behind the Verified Query Repository supporting Cortex Analyst and natural-language analytics
- Own the context layer that makes Marketing's data interpretable to analysts and AI agents, including entity descriptions, metric documentation, approved analytical uses, and known limitations of each source
- Diagnose discrepancies across Snowflake, BI, Adobe Analytics, marketing platforms, finance reporting, and operational systems
- Identify gaps in source data, identity resolution, event structures, and dimensional consistency before they become downstream reporting issues
- Define ownership boundaries between source systems, engineered data products, semantic models, and reporting layers
- Lead, coach, and develop a team of three to four analysts spanning paid media, customer and lifecycle analytics, and digital analytics
- Set the Marketing Analytics roadmap, allocate the team against it, and manage intake and prioritization from across Marketing
- Establish and enforce analytical standards including SQL quality, peer review, testing, documentation, and reuse
- Review the team's models, queries, and analyses before they reach stakeholders, and hold the quality bar for what gets published
- Grow the team's depth in dimensional modeling, data modeling practice, and analytical rigor through direct coaching and review
- Manage performance, hiring, and development planning for the team
- Define and govern core marketing KPIs including revenue, conversion, CAC and CPA, ROAS, retention, funnel progression, pacing to plan, and channel efficiency
- Own the integrity and narrative of recurring executive performance reporting, including monthly business reviews and QBR analytics, with the team producing the underlying analysis
- Determine how platform-reported, attribution-based, financial, and incrementality-based measures reconcile, and establish where each is appropriately used
- Lead attribution, cohort, and incrementality work so it produces decision-grade guidance on marketing investment, building the harder analyses directly
- Own predictive modeling for Marketing, including membership churn and renewal propensity, certification lapse and recertification likelihood, and product and cross-sell propensity, alongside segmentation, lifecycle, and cohort analysis across CRM, web, campaign, commerce, certification, and membership data
- Define the features, target definitions, validation approach, and performance thresholds for predictive models, determine when a score is reliable enough to act on, and establish how scores are refreshed, monitored for drift, and activated in campaign, lifecycle, and retention programs
- Set analytical standards for A/B testing, holdouts, geo experiments, and conversion-lift studies within the marketing analytics practice, including hypotheses, test populations, success metrics, and readouts
- Partner with Product and Marketing to define measurable funnels, customer states, lifecycle stages, and behavioral signals for considered-purchase journeys, and hold a clear distinction between attribution, correlation, prediction, and causal measurement when results are communicated
- Govern Marketing's analytical products in enterprise BI and visualization platforms, ensuring definitions are sourced from the semantic layer rather than rebuilt in the tool
- Use Adobe Analytics to investigate digital behavior, traffic, customer journeys, and conversion performance, and work with data from Adobe Experience Platform, CRM, commerce, and paid media systems
- Apply working knowledge of digital event instrumentation, tagging, identity, and schemas to identify measurement and architecture issues at the source, and establish where AI-assisted tooling is applied to SQL development, analysis, documentation, and testing
- Expand governed self-service and AI-assisted analytics for Marketing across Snowflake, BI, and natural-language surfaces, and represent Marketing data and context requirements in the Marketing AI reference architecture
- Define what Marketing data and context AI agents and natural-language tools are permitted to reach, at what grain, and under what access and accuracy controls, and own the evaluation approach that tests answer accuracy before release and monitors it as data and definitions change
- Serve as Marketing's primary analytical counterpart to Data Engineering, Data Architecture, and Data Governance
- Translate business questions into analytical requirements and data-product specifications that engineering can build against
- Partner with Data Engineering on transformation logic, data contracts, source integration, model design, and data-quality requirements
- Partner with Performance Marketing, Brand, Digital, Product, Lifecycle, and Regional Marketing on measurement needs and analytical priorities
- Present analytical findings, performance trends, and recommendations to senior leadership
Skills
- 10+ years across marketing analytics, customer analytics, analytics engineering, or related disciplines, including prior leadership of an analytics team
- Demonstrated experience leading and developing an analytics team while remaining hands-on technically
- Working knowledge of ETL and ELT concepts, transformation pipelines, data dependencies, and data-quality controls, with the ability to read, review, and challenge logic developed by engineering teams
- Experience working within layered, dimensional, or medallion data architectures
- Experience integrating and analyzing data across CRM, marketing, digital, commerce, and transactional systems
- Customer data platform architecture including identity resolution, event schema design, and audience activation
- Marketing measurement including attribution, experimentation, incrementality, conversion, CAC and CPA, and ROAS, and experience directing or building predictive and propensity models such as churn or retention
- Advanced SQL including complex joins, CTEs, window functions, transformations, data profiling, reconciliation, and validation, applied to large-scale datasets
- Working hands-on experience with Snowflake or another modern cloud data platform, sufficient to profile, query, validate, and reconcile at scale
- Demonstrated experience designing business and analytical data models, and specifying them for engineering teams to build against
- Dimensional modeling including facts, dimensions, grain, cardinality, slowly changing dimensions, hierarchies, and conformed dimensions
- Metric governance, lineage, and reconciliation across multiple enterprise systems
- Strong analytical storytelling and executive communication
- Development and governance of analytical products in an enterprise BI platform, preferred
- Familiarity with semantic layer and context technologies such as Snowflake Semantic Views, Cortex Analyst, Cortex Search, dbt Semantic Layer, or Looker, preferred
- Digital analytics using an enterprise web analytics platform, preferred
- Dbt or comparable SQL transformation framework, preferred
- Python or R for advanced analysis, automation, or modeling, preferred
- Git and version-controlled analytics development, preferred
- Data testing and observability frameworks, preferred
- Marketing mix modeling, geo experimentation, conversion lift, causal-inference methods, and machine learning applied to churn, propensity, or lifetime value, preferred
Benefits
- Skill development opportunities, to help you grow now and into the future
- Access to a global network, to enrich your professional experience
- Flexible options to help balance work time and your time
- Award and bonus opportunities
Company Overview
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