This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Analytics Engineer – Marketing Data based in Germany.

Join a data-driven environment where analytics engineering plays a key role in transforming marketing data into actionable business insights.

In this role, you will design and optimize data models that support marketing analytics and business decision-making.

You will work extensively with Google Analytics data, Snowflake, SQL, and Python to build reliable, scalable data solutions.

Partnering with business stakeholders, you will translate complex requirements into clear technical deliverables.

You will help ensure data quality, consistency, and accuracy throughout the entire data pipeline.

Working within a collaborative, version-controlled development environment, you will contribute to robust engineering practices and continuous improvement.

This opportunity is ideal for an experienced analytics engineer who combines strong technical expertise with business understanding and a proactive, problem-solving mindset.

Accountabilities

• Design, develop, and maintain scalable data models, transformations, and materializations in Snowflake to support marketing analytics and business reporting.
• Work hands-on with Google Analytics data in a production environment, ensuring that marketing data is accurately structured and readily available for analysis.
• Develop, review, troubleshoot, and optimize SQL queries and Python code to improve data processing, reliability, and performance.
• Analyze API documentation and translate endpoints, objects, fields, and identifiers into practical data integration solutions aligned with business requirements.
• Implement and maintain data validation practices across multiple stages of data pipelines to ensure data integrity, consistency, and accuracy.
• Apply Git-based development workflows, including version control, code reviews, testing, and CI/CD practices, to maintain high engineering standards.
• Collaborate with stakeholders to gather requirements, clarify ambiguous business needs, structure discussions, and translate findings into precise, actionable technical specifications.
• Identify opportunities to improve data models, transformation logic, pipeline reliability, and overall analytics engineering processes.
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