Design, build, and maintain high-quality dimensional data models and ELT pipelines that support marketing analytics across Paid Marketing, SEO, and Retailer Marketing, and attribution.
Work closely with Data Scientists, Analysts, Data Engineers, and Marketing stakeholders to understand analytical needs, translate business questions into data requirements, and deliver trusted, decision-ready data assets.
Lead efforts in data modeling, metric definition, testing, and documentation, owning data quality and resolving issues at their root cause across critical marketing workflows.
Set standards and guide execution for marketing data pipelines, reviewing designs and implementations to ensure reliability, scalability, and analytical usability.
Improve and evolve existing marketing data pipelines and workflows to reduce manual effort, improve performance, and increase the speed and confidence of analysis.
Develop scalable analytics engineering patterns, including dimensional models, testing frameworks, and monitoring approaches, to support evolving marketing measurement needs.

Requirements

5+ years of experience in Analytics Engineering, Data Engineering, or closely related roles, with clear ownership of production data systems.
Advanced SQL skills and deep experience designing well-architected dimensional data models (star schemas, fact/dimension tables, SCDs).
Hands-on experience with the modern data stack, including dbt, Snowflake, and Airflow.
Strong understanding of marketing data and metrics, including paid media performance, attribution concepts, and channel-level measurement.
Demonstrated experience providing technical leadership on complex data projects, including setting standards, guiding execution, and supporting the growth of other engineers.
Excellent judgment and product thinking — you know how to balance speed, correctness, and long-term maintainability.
Experience working directly with Data Scientists on experimentation, attribution, or incrementality measurement (preferred).
Familiarity with marketing platforms (ie Google Ads, Meta, Google Analytics, SEO tooling) (preferred).
Python experience for automation or advanced transformations (preferred).
Experience introducing analytics engineering best practices in an organization that didn't previously have them (preferred).

Benefits

Remote work policy
New hire equity grant
Annual refresh grants

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLdimensional data modelsstar schemasfact tablesdimension tablesslowly changing dimensions (SCDs)analytics engineeringdata modelingdata qualitydata pipelines

Soft Skills

technical leadershipjudgmentproduct thinkingproblem-solvingcollaborationcommunicationmentorshipexecution guidanceanalytical thinkingattention to detail