Bridge the gap between marketing strategy and data execution by designing, building, and maintaining data pipelines and models that power marketing analytics, attribution, and reporting, enabling marketing teams to make faster, smarter, and more measurable decisions.
Requirements
- Strong SQL skills and experience working with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift)
- Experience building ELT/ETL pipelines using tools such as Fivetran, OpenFlow, Airbyte, dbt, or similar
- Familiarity with marketing platforms and metrics (CAC, LTV, funnel conversion, attribution models, ROAS)
- Experience supporting BI tools (Sigma, Looker, Tableau, Power BI, etc.)
- Experience with dbt and analytics engineering best practices
- Python experience for data transformations or automation
- Understanding of multi-touch attribution and marketing measurement frameworks
Responsibilities
- Design and maintain scalable data pipelines for marketing data sources (e.g., CRM, ad platforms, web analytics, email tools)
- Integrate data from platforms such as Braze, Google Analytics, HubSpot, Salesforce, Google Ads, Meta, LinkedIn, and similar tools
- Build and maintain end-to-end marketing data pipelines into Snowflake
- Model and transform marketing data using dbt, following Analytics Engineering best practices
- Create and maintain analytics-ready marts to support marketing performance, attribution, and funnel analysis
- Support and optimize Sigma Computing dashboards, enabling true self-service analytics
- Implement testing, documentation, and freshness monitoring within dbt
Other
- 5+ years of experience in data engineering, analytics engineering, or a similar role
- Strong problem-solving skills and attention to data accuracy
- Ability to communicate technical concepts to non-technical stakeholders
- Experience working in a fast-growing or data-maturing organization
- Flexible work schedules
