Manager, Marketing Data & Analytics

Responsibilities

• The Manager, Marketing Data and Analytics owns the business data architecture and metric definitions behind Marketing's numbers, and leads the team that measures Marketing. The role defines how marketing, customer, product, and revenue data should be modeled, decides what each metric means, and ensures those definitions are implemented consistently across Snowflake, BI, and natural-language analytics.
• The role combines hands-on technical depth with team leadership. The individual is expected to be advanced in SQL, to design analytical data models, and to profile, reconcile, and diagnose data directly, so they can specify what engineering builds and verify that the result represents the business correctly. They are equally expected to lead three to four analysts, set the standards those analysts work to, and develop the team's technical capability.
• Data Engineering and the Snowflake team own ingestion, infrastructure, orchestration, transformation pipelines, and semantic view implementation. This role represents Marketing in the design of the data layer, writes the specifications those teams build against, and is accountable for ensuring the resulting data products correctly represent the business.
• 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.

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, 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.
• Development and governance of analytical products in an enterprise BI platform, preferred.
• 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.

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

• an excellent total package, with compensation and benefits based upon your geographic location.
• 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.