JOB DESCRIPTION:

We are seeking a highly skilled and collaborative Marketing Data Analyst to bridge the gap between data engineering and data analysis. In this role, you’ll transform raw data into clean, tested, and well-documented datasets that enable self-service analytics and strategic decision-making across the organization.
You’ll work closely with data engineers, analysts, and business stakeholders to design and maintain data models, pipelines, and analytics infrastructure that support scalable, reliable, and insightful data use.
This role is a part of the Growth Marketing function within the marketing department, directly reporting into the Senior Director, Growth Marketing.

QUALIFICATIONS:

Required:

• 1-3+ years of experience in analytics engineering, data engineering, or analytics-focused roles
• Proficiency with SQL and data modeling best practices (e.g., medallion schemas)
• Proficient in branding strategies, Git/GitHub workflows and collaborative development
• Experience with dbt, Airflow, or similar ELT/ETL tools
• Strong understanding of modern data warehouses (e.g.,Databricks)
• Strong understanding of modern marketing concepts and platforms (e.g. CDPs, Clean Rooms, Lifecyle Marketing)
• Experience with Git and version control for analytics workflows
• Familiarity with BI tools (e.g., Power BI)
• Excellent communication and stakeholder management skills

Preferred:

• Experience in a cloud-based data environment (e.g., Azure)
• Exposure to programming languages like Python or R
• Familiarity with CI/CD pipelines and data testing frameworks (e.g., Great Expectations, dbt tests)
• Prompt engineering skills for code generation and problem solving
• Background in statistics, analytics, or computer science

• Pipeline Development: Establish data pipelines for key marketing sources
• Model Development : Build and maintain scalable, reusable, and well-documented data models using tools like dbt (or equivalent).
• Build or maintain computed or predictive models to advance marketing effectiveness
• Data Transformation : Design ELT processes to transform raw data into structured, analytics-ready tables.
• Quality Assurance : Implement data validation and testing frameworks to ensure high data integrity and reliability.
• Cross-functional Collaboration : Work with analysts, data engineers, and business teams to understand data needs and ensure alignment with analytical goals.
• Performance Optimization : Monitor query and pipeline performance, identifying opportunities to improve efficiency and reduce cost.
• Documentation & Enablement : Maintain clear documentation for data models and pipelines, and support the analytics team in using data effectively.
• Data Governance : Contribute to data governance practices, ensuring consistency, privacy, and compliance across datasets.
• Data Analysis : Support Data analysis when/where needed