Senior Data Engineer - Zalando Poland
Summary
Senior Data Engineer (via Zoolatech, for partner Zalando) building the customer-identity data foundation in Wrocław: designing PySpark/Databricks pipelines to reconcile customer identifiers across web, app and CRM channels, with LLM-assisted cleanup and GDPR-compliant data quality. Core stack: PySpark, SQL, Databricks, Airflow, Kafka.
Together with our partner, a leading European online fashion & beauty retailer, we’re looking for a Senior Data Engineer to join a new initiative focused on customer identity and data interoperability. Customers interact with the platform across web, app, CRM and marketing channels, and each system identifies them differently. You’ll help design and build the data foundation that connects and reconciles these identifiers. This enables reliable customer journey analytics, personalisation and privacy-compliant data usage at scale.
Part of the role is applying AI to the data itself. You’ll use LLM-based tooling and automation to speed up repetitive work such as data cleanup, deduplication, anomaly detection and data quality validation across large customer datasets.
Responsibilities
Design and build scalable pipelines that ingest, normalise and reconcile customer identifiers from multiple sources
Develop identity mapping and stitching logic, for example anonymous-to-logged-in and cross-device
Lead data cleanup and migration of legacy identifier data, using AI-assisted automation to reduce manual effort
Define and implement data quality checks, validation rules and monitoring for identity data
Build in privacy, consent and data retention requirements (GDPR) from the start
Work closely with backend engineers, analysts and data consumers to define data contracts and downstream usability
Contribute to technical design, documentation and engineering standards in a newly forming team
Requirements
Must-have:
5+ years of experience as a Data Engineer or in a similar role
Strong hands-on experience with PySpark/Apache Spark and SQL
Production experience with Databricks or similar systems
Experience with pipeline orchestration (e.g. Airflow)
Solid understanding of data modelling, data quality and large-scale distributed data processing
Practical experience using AI/LLM tools to automate data engineering tasks, such as cleanup, classification, validation or code generation
Experience with event streaming (Kafka or similar)
Upper-Intermediate English or higher
Nice-to-have:
Experience with identity resolution, customer data platforms (CDP) or master data management
Experience with privacy-sensitive data (pseudonymisation, hashing, consent management)
Experience in e-commerce or retail tracking/analytics