Data Engineer

Data Engineer
- Databricks / Azure

Location:
Malmö, Sweden Mode: Hybrid Duration
- 12 Months
- Extendable Talent Partner: Rediflex AB Assignment Overview We are looking for an experienced Data Engineer to join a data-driven transformation initiative within the Retail Concept Operations (RCO) area of a leading global retail organisation. The team is developing digital products for Customer Meeting Point (CMP) Performance Evaluation and Franchisee Performance Evaluation. The objective is to consolidate data from multiple business systems into trusted, scalable and automated data products that support reporting, analytics, performance management, business decision-making and strategic planning. As a Data Engineer, you will design, develop and maintain the data foundation supporting these products, helping transform fragmented and manual reporting into a reliable and centralised analytics ecosystem.

Key Responsibilities
• Design and develop end-to-end data pipelines covering data ingestion, transformation and serving.
• Work with multiple data sources to create reliable, scalable and reusable data products.
• Contribute throughout the complete data engineering lifecycle, from ideation and requirements analysis through design, development, testing, deployment and operational support.
• Design and implement high-level and low-level data architectures and technical solutions.
• Apply best practices in data modelling and lakehouse architecture.
• Develop and maintain data solutions using Databricks, Apache Spark and Delta Lake.
• Implement batch and streaming data pipelines where required.
• Build and maintain Databricks jobs, workflows and orchestration.
• Perform performance optimisation, troubleshooting and production monitoring of data pipelines.
• Ensure data accuracy through data validation, quality checks, monitoring and auditability.
• Implement version control, automated testing and deployment processes using Git and CI/CD.
• Support environment management and automated promotion across development, test and production environments.
• Apply Infrastructure as Code (IaC) principles for data platform components.
• Ensure solutions are secure, maintainable, cost-efficient and aligned with cloud-native data engineering standards.
• Participate in sprint planning, estimation, technical documentation and knowledge sharing.
• Collaborate with business and technical stakeholders and clearly communicate architectural decisions, data flows and technical trade-offs.
• Provide operational follow-up and continuous improvement of production data solutions. Required Skills & Experience
• Strong professional experience in Data Engineering and modern data platforms.
• Hands-on experience with Databricks, including:
• Apache Spark
• Delta Lake
• Databricks notebooks
• Job/workflow orchestration
• Performance optimisation
• Environment management
• Strong knowledge of Microsoft Azure data services, particularly Azure Data Lake Storage (ADLS).
• Proven experience designing and implementing end-to-end data pipelines.
• Strong understanding of Lakehouse architecture and data modelling.
• Experience with both batch and streaming data processing.
• Strong understanding of data quality, validation, monitoring and troubleshooting.
• Practical experience with DevOps practices, including:
• Git/source control
• CI/CD pipelines
• Automated testing
• Environment promotion
• Infrastructure as Code
• Experience with technical design, solution architecture and requirements specification.
• Ability to work with stakeholders throughout the full software/data engineering lifecycle.
• Strong understanding of scalable, resilient, reusable and cost-efficient cloud-native architectures.
• Strong communication skills and ability to explain technical concepts to non-technical stakeholders. Main

Skills:
Data Engineering | Databricks | Azure | Apache Spark | Delta Lake | Azure Data Lake CI/CD| Data Pipelines | Lakehouse | Data Modelling | DevOps | CI/CD | Git | Data Validation | Performance Optimisation