Manager, Marketing Data – Analytics

Job not on LinkedIn

🕒 October 22

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

📉 Data Analyst

👻 Ghost score 10%

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PMI

WebsiteLinkedInAll Job Openings

1 - 1 employees

📚 Education

🤝 Non-profit

Education • Non-profit

PMI is the Project Management Institute, a global nonprofit professional association that sets standards, provides education and credentials, and supports the practice of project and program management. PMI offers widely recognized certifications (PMP, CAPM, PMI-ACP, PMI-CPMAI, and others), publishes standards and guides such as the PMBOK Guide, runs training, events, research and thought leadership, and provides digital tools and enterprise solutions (e. g. , PMI Infinity) and membership services to a community of project professionals (noted as more than 800,000 members).

📋 Description

• 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

• 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

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