Data Engineer
HybridSolna, Stockholm, Sweden
Job Summary
Design and implement scalable batch and real-time data pipelines using Azure-native technologies, including Azure Data Factory, Databricks, and Delta Lake. Build robust ingestion frameworks from multiple sources and manage Medallion Architecture within the enterprise Data Lake. Develop transformation frameworks with PySpark and enable CI/CD pipelines using Azure DevOps best practices. Collaborate with stakeholders to deliver trusted data products while ensuring adherence to governance, security, and operational excellence standards. Drive platform optimization focused on performance, scalability, and cost efficiency. Join the Data Platform team within Coop's AI & Business Intelligence organisation, supporting analytics, reporting, and operational intelligence across the company's 4.2 million members.
Required Qualifications
- Have strong hands-on experience with the Azure data platform – Azure Data Factory, Azure Databricks, Data Lake Storage Gen2 (ADLS), Azure Functions, Event Hub and Service Bus.
- Are highly skilled in Databricks and the Lakehouse – PySpark, Spark SQL, Delta Lake, Databricks Workflows, Unity Catalog and performance tuning, applying Medallion and Lakehouse architecture in practice.
- Are strong in data architecture and modelling – Lakehouse and Medallion design, ETL/ELT frameworks, and data quality & validation frameworks.
- Have experience with streaming and integration – Apache Kafka, event-driven architecture, Service Bus, batch ingestion / Autoloader and REST APIs.
- Are a confident programmer in Python, PySpark and SQL.
- Work the DevOps way – Azure DevOps, CI/CD pipeline development and Infrastructure as Code (Terraform preferred).
- Are comfortable with monitoring and operations – Log Analytics, Application Insights, and performance tuning & troubleshooting.
- Can communicate effectively with both technical specialists and non-technical stakeholders.
- Have 5+ years of experience in data engineering.
- Have built enterprise-scale Azure data platforms hands-on.
- Have worked with retail, logistics or large-scale operational data environments.
- Have supported machine learning and advanced analytics workloads.
- Have a strong understanding of distributed data processing and streaming architectures.
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