Manager, Marketing Data & Analytics

Project Management Institute

2d•$142,800 - $174,500

About The Position

The Manager, Marketing Data & Analytics owns Marketing’s business data architecture, canonical metric definitions, and the context behind Marketing’s data. The role specifies how marketing, customer, product, and revenue data should be modeled; defines what metrics mean; and ensures those definitions are implemented consistently across Snowflake, BI, and natural-language analytics. It combines advanced, hands-on SQL and analytical modeling with leadership of a team of three to four analysts. The manager profiles, reconciles, and diagnoses data directly, specifies work for engineering teams, and validates that resulting data products accurately represent the business. The role represents Marketing in designing its data layer and partners with Data Engineering and the Snowflake team, which own ingestion, infrastructure, orchestration, transformation pipelines, and semantic view implementation. The manager leads the Marketing Analytics roadmap and team standards, owns marketing measurement and predictive-modeling practices, and is accountable for executive performance reporting and governed analytical products across certification, membership, e-learning, events, and other product lines. The position also expands governed self-service and AI-assisted analytics, defines the data and context AI agents may access and the controls for that access, and establishes evaluations to test answer accuracy. The manager is Marketing’s primary analytical counterpart to Data Engineering, Data Architecture, and Data Governance, and presents findings and recommendations to senior leadership. Up to 10% travel may be required.

Requirements

At least 10 years of experience 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, including the ability to review and challenge engineering logic.
Experience working within layered, dimensional, or medallion data architectures.
Experience integrating and analyzing data across CRM, marketing, digital, commerce, and transactional systems.
Experience with customer data platform architecture, including identity resolution, event schema design, and audience activation.
Marketing measurement experience covering attribution, experimentation, incrementality, conversion, CAC and CPA, and ROAS, plus 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 on large-scale datasets.
Hands-on experience with Snowflake or another modern cloud data platform sufficient to profile, query, validate, and reconcile data at scale.
Demonstrated experience designing business and analytical data models and specifying them for engineering teams.
Dimensional modeling knowledge, including facts, dimensions, grain, cardinality, slowly changing dimensions, hierarchies, and conformed dimensions.
Experience with metric governance, lineage, and reconciliation across multiple enterprise systems.
Strong analytical storytelling and executive communication skills.

Nice To Haves

Experience developing and governing analytical products in an enterprise BI platform.
Familiarity with semantic-layer and context technologies such as Snowflake Semantic Views, Cortex Analyst, Cortex Search, dbt Semantic Layer, or Looker.
Digital analytics experience using an enterprise web analytics platform.
Experience with dbt or a comparable SQL transformation framework.
Python or R experience for advanced analysis, automation, or modeling.
Experience with Git and version-controlled analytics development.
Experience with data testing and observability frameworks.
Experience with marketing mix modeling, geo experimentation, conversion lift, causal-inference methods, or machine learning applied to churn, propensity, or lifetime value.

Responsibilities

Own Marketing’s canonical metric definitions and business logic for the governed semantic layer, resolving disagreements across marketing, finance, and reporting platforms.
Specify complex, cross-domain semantic models and validate Snowflake team implementations against the specifications.
Define Marketing’s business data architecture, including facts, dimensions, grain, relationships, hierarchies, and conformed dimensions across marketing, customer, product, transaction, and engagement data.
Design analytical models for questions spanning acquisition, engagement, conversion, certification, membership, renewal, and revenue.
Use advanced SQL to profile data, validate sources, reconcile metrics, build reusable datasets, and investigate customer and business questions.
Determine which unstructured materials must be retrievable alongside structured metrics; maintain source-to-metric mappings, lineage, and the context layer for analysts and AI agents.
Own business questions, expected results, and synonym glossaries for the Verified Query Repository supporting Cortex Analyst and natural-language analytics.
Diagnose discrepancies across Snowflake, BI, Adobe Analytics, marketing platforms, finance reporting, and operational systems; identify source-data, identity-resolution, event-structure, and dimensional-consistency gaps.
Define ownership boundaries among source systems, engineered data products, semantic models, and reporting layers.
Lead, coach, and develop three to four analysts covering paid media, customer and lifecycle analytics, and digital analytics.
Set the Marketing Analytics roadmap, allocate team capacity, and manage intake and prioritization across Marketing.
Establish and enforce analytical standards for SQL quality, peer review, testing, documentation, and reuse; review the team’s models, queries, and analyses before publication.
Develop the team’s dimensional modeling, data modeling, and analytical capabilities; manage performance, hiring, and development planning.
Define and govern 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.
Reconcile platform-reported, attribution-based, financial, and incrementality-based measures and establish appropriate uses for each.
Lead attribution, cohort, and incrementality work, building complex analyses as needed to guide marketing investment.
Own predictive modeling for churn, renewal propensity, certification lapse and recertification likelihood, product and cross-sell propensity, and related segmentation, lifecycle, and cohort analysis.
Define predictive-model features, targets, validation approaches, and performance thresholds; decide when scores are reliable enough to use and establish refresh, drift monitoring, and activation practices.
Set standards for A/B testing, holdouts, geo experiments, and conversion-lift studies, including hypotheses, test populations, success metrics, and readouts.
Partner with Product and Marketing to define measurable funnels, customer states, lifecycle stages, and behavioral signals, and distinguish attribution, correlation, prediction, and causal measurement in communications.
Govern Marketing analytical products in enterprise BI and visualization platforms, ensuring metrics come from the semantic layer rather than being recreated in tools.
Use Adobe Analytics to investigate digital behavior, traffic, customer journeys, and conversion; work with Adobe Experience Platform, CRM, commerce, and paid media data.
Apply knowledge of digital instrumentation, tagging, identity, and schemas to identify source measurement and architecture issues; establish uses for AI-assisted tooling in SQL development, analysis, documentation, and testing.
Expand governed self-service and AI-assisted Marketing analytics across Snowflake, BI, and natural-language surfaces; represent Marketing requirements in the Marketing AI reference architecture.
Define AI-agent and natural-language-tool access to Marketing data and context, including grain, access and accuracy controls; evaluate answer accuracy before release and monitor 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; 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.
Travel up to 10% if required.