Manager, Marketing Data – Analytics

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of Marketing's canonical metric definitions and the business logic for the governed semantic layer.

• Define semantic models for intricate, cross-domain marketing use cases and verify the implementations by the Snowflake team.

• Manage Marketing's business data architecture, encompassing facts, dimensions, grain, relationships, hierarchies, and conformed dimensions.

• Create analytical models that span acquisition, engagement, conversion, certification, membership, renewal, and revenue.

• Write advanced SQL to profile data, validate sources, reconcile metrics, construct reusable datasets, and address complex inquiries.

• Maintain mappings from source to metric, lineage, documentation, entity descriptions, approved analytical uses, and known source limitations.

• Take charge of business questions, expected outcomes, and a synonym glossary for the Verified Query Repository that supports Cortex Analyst and natural-language analytics.

• Identify discrepancies across Snowflake, BI, Adobe Analytics, marketing platforms, finance reporting, and operational systems.

• Spot gaps in source data, identity resolution, event structure, and dimensional consistency.

• Define ownership boundaries across source systems, engineered data products, semantic models, and reporting layers.

• Lead, mentor, and develop a team of three to four analysts.

• Establish the Marketing Analytics roadmap, allocate resources, and manage intake and prioritization.

• Set standards for SQL quality, peer review, testing, documentation, reuse, modeling, and analytical rigor.

• Review models, queries, and analyses prior to stakeholder publication.

• Oversee team performance, hiring, and development planning.

• Define and govern marketing KPIs, including revenue, conversion, CAC, CPA, ROAS, retention, funnel progression, pacing, and channel efficiency.

• Ensure 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 analyses related to attribution, cohort, incrementality, predictive modeling, segmentation, lifecycle, and retention.

• Specify predictive model features, targets, validation, performance thresholds, refresh schedules, drift monitoring, and activation controls.

• Establish standards for A/B tests, holdouts, geo experiments, and conversion-lift studies.

• Collaborate with Product and Marketing on funnels, customer states, lifecycle stages, and behavioral signals.

• Govern Marketing analytical products in enterprise BI and visualization platforms.

• Utilize Adobe Analytics and data from Adobe Experience Platform, CRM, commerce, and paid media systems to analyze digital behavior and conversion.

• Apply expertise in digital instrumentation, tagging, identity, and schemas to pinpoint measurement and architecture issues.

• Expand governed self-service and AI-assisted analytics across Snowflake, BI, and natural-language interfaces.

• Define access, grain, accuracy controls, and evaluation criteria for AI agents and natural-language tools.

• Act as Marketing's analytical liaison to Data Engineering, Data Architecture, and Data Governance.

• Translate business inquiries into analytical requirements and data product specifications.

• Collaborate with engineering on transformation logic, data contracts, source integration, model design, and data quality.

• Work alongside Marketing functions to address measurement needs and priorities.

• Present analytical findings, performance trends, and recommendations to senior leadership.

⛳️ Requirements

• 10+ years of experience in marketing analytics, customer analytics, analytics engineering, or related fields, including previous leadership of an analytics team.

• Proven track record of leading and developing an analytics team while maintaining a hands-on technical approach.

• Familiarity with ETL and ELT concepts, transformation pipelines, data dependencies, and data quality controls.

• Experience with layered, dimensional, or medallion data architectures.

• Proficient in integrating and analyzing data across CRM, marketing, digital, commerce, and transactional systems.

• Understanding of customer data platform architecture, including identity resolution, event schema design, and audience activation.

• Knowledge of marketing measurement techniques such as attribution, experimentation, incrementality, conversion, CAC, CPA, and ROAS.

• Experience in leading or developing predictive and propensity models, such as those for churn or retention.

• Advanced SQL skills, including complex joins, CTEs, window functions, transformations, data profiling, reconciliation, and validation, applied to large datasets.

• Hands-on experience with Snowflake or another modern cloud data platform.

• Demonstrated ability to design business and analytical data models and specify them for engineering teams.

• Proficiency in dimensional modeling, including facts, dimensions, grain, cardinality, slowly changing dimensions, hierarchies, and conformed dimensions.

• Experience with metric governance, lineage, and reconciliation across multiple enterprise systems.

• Strong skills in 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 is preferred.

• Experience with digital analytics using an enterprise web analytics platform is preferred.

• Proficiency in dbt or a comparable SQL transformation framework is preferred.

• Knowledge of Python or R for advanced analysis, automation, or modeling is preferred.

• Familiarity with Git and version-controlled analytics development is preferred.

• Understanding of data testing and observability frameworks is preferred.

• Experience with marketing mix modeling, geo experimentation, conversion lift, causal inference methods, and machine learning applied to churn, propensity, or lifetime value is preferred.

• Willingness to travel up to 10% if required.

🏝️ Benefits

• Competitive salary and performance incentives.

• Comprehensive health and wellness benefits.

• Opportunities for professional development and growth.

• Flexible working arrangements.

• Collaborative and innovative work environment.