Manager, Marketing Data – Analytics

PMI

. Own Marketing's canonical metric definitions and business logic for the governed semantic layer .

Posted 10/22/2026full-timeRemote • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in Marketing Analytics, including advanced SQL, predictive modeling, and metric governance, combined with strong leadership skills to develop and manage analytics teams effectively. Proficient in data architecture and integration across various marketing and transactional systems to drive actionable insights.

Highest-signal resume keywords

Advanced SQLPredictive ModelingMarketing MeasurementData ArchitectureTeam Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Advanced SQLPredictive ModelingAttribution AnalysisData GovernanceDimensional ModelingData Quality ControlsMetric ReconciliationData ProfilingCustomer Data Platform ArchitectureDigital Analytics

Soft Skills

Analytical StorytellingExecutive CommunicationTeam DevelopmentResource AllocationStakeholder Management

Tools & Technologies

SnowflakeAdobe AnalyticsCRM SystemsData Testing FrameworksDbtLookerCortex AnalystGitAI-Assisted AnalyticsEnterprise BI Platforms

Industry Keywords

Marketing AnalyticsCustomer AnalyticsAnalytics EngineeringDigital BehaviorConversion MetricsEvent Schema DesignIncrementalityCustomer Acquisition CostReturn on Ad SpendChurn Analysis

Tech Stack

Tools & technologies

CloudETLPythonSQL

About the role

Key responsibilities & impact

Own Marketing's canonical metric definitions and business logic for the governed semantic layer
Specify semantic models for complex, cross-domain marketing use cases and validate Snowflake team implementations
Own Marketing's business data architecture, including facts, dimensions, grain, relationships, hierarchies, and conformed dimensions
Design analytical models 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 questions
Maintain source-to-metric mappings, lineage, documentation, entity descriptions, approved analytical uses, and known source limitations
Own business questions, expected results, and synonym glossary 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 across source systems, engineered data products, semantic models, and reporting layers
Lead, coach, and develop a team of three to four analysts
Set the Marketing Analytics roadmap, allocate resources, and manage intake and prioritization
Establish standards for SQL quality, peer review, testing, documentation, reuse, modeling, and analytical rigor
Review models, queries, and analyses before stakeholder publication
Manage team performance, hiring, and development planning
Define and govern marketing KPIs including revenue, conversion, CAC, CPA, ROAS, retention, funnel progression, pacing, 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
Lead attribution, cohort, incrementality, predictive modeling, segmentation, lifecycle, and retention analyses
Define predictive-model features, targets, validation, performance thresholds, refresh schedules, drift monitoring, and activation controls
Set standards for A/B tests, holdouts, geo experiments, and conversion-lift studies
Partner with Product and Marketing on funnels, customer states, lifecycle stages, and behavioral signals
Govern Marketing analytical products in enterprise BI and visualization platforms
Use Adobe Analytics and data from Adobe Experience Platform, CRM, commerce, and paid media systems to investigate digital behavior and conversion
Apply knowledge of digital instrumentation, tagging, identity, and schemas to identify measurement and architecture issues
Expand governed self-service and AI-assisted analytics across Snowflake, BI, and natural-language surfaces
Define access, grain, accuracy controls, and evaluation for AI agents and natural-language tools
Serve as Marketing's analytical counterpart to Data Engineering, Data Architecture, and Data Governance
Translate business questions into analytical requirements and data-product specifications
Partner with engineering on transformation logic, data contracts, source integration, model design, and data quality
Partner with Marketing functions on measurement needs and priorities
Present analytical findings, performance trends, and recommendations to senior leadership

Requirements

What you’ll need

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
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
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
Demonstrated experience designing business and analytical data models and specifying them for engineering teams
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
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
Up to 10% travel if required