Data Engineer – Commerce and Customer Data
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Job Description
Data Engineer developing scalable commerce and customer data products for INTERSPORT’s e-commerce unit. Building ETL/ELT pipelines supporting analytics, product development, and AI applications.
Responsibilities:
- Develop and operate scalable data products as well as ETL and ELT pipelines for commerce and customer domains
- Model data from checkout, orders, payments, customer, loyalty, and customer service systems
- Make data available to product teams and analytics
- Integrate data from different systems and event-based sources, including via Kafka
- Develop high-performance, transparent, and reusable data models using the modern data stack
- Ensure data quality, governance, and privacy, particularly for personal customer and payment data
- Work closely with Product Management, Software Engineering, Data, and Business teams
- Own data products from requirements through production operations
- Actively contribute to the Data Engineering practice and further develop shared standards and ways of working
Requirements:
- Several years of experience in data engineering or a comparable role
- Very strong knowledge of SQL and Python
- Experience with ETL/ELT and data modeling
- Experience with a modern data stack, ideally Snowflake, dbt, and Prefect
- Experience with Kafka or comparable event and streaming technologies
- Knowledge of AWS
- Ideally, experience with Infrastructure as Code using Pulumi or Terraform
- Good understanding of data quality, data governance, and the handling of personal data
- Experience collaborating with cross-functional product or development teams
- Independent, structured, and solution-oriented approach to work
- German at least at B2 level and confident English skills
- Nice to have: Experience with e-commerce, checkout, order, or payment data
- Nice to have: Knowledge of customer data, CRM, loyalty, or customer service
- Nice to have: Experience with Data Mesh and domain-oriented data products
- Nice to have: Knowledge of BI tools such as Looker, Metabase, or Power BI
- Nice to have: Experience building data pipelines for AI or machine learning applications
Benefits:
- Flexible working hours
- Remote and mobile working options
- Professional development opportunities
- Employee discounts
- Additional benefits
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