Arbetsbeskrivning

Avaron AB is a growing consultancy that matches your expertise with the market's most interesting assignments, offering a platform where your professional development is central.

You will join a team building the data foundation for two performance evaluation products in a global retail environment. The assignment focuses on creating a unified and reliable data platform that consolidates information from multiple operational systems and supports faster decision-making across a complex omnichannel business. You will help replace fragmented and manually managed data flows with scalable pipelines, automated validation, and robust data models that improve quality, consistency, and timeliness.

• Design, build, and maintain data pipelines that connect, collect, organize, version, and consolidate data from multiple operational sources.
• Develop scalable data models and ingestion frameworks for a unified data platform.
• Create resilient batch and streaming pipelines across ingestion, transformation, and serving layers.
• Implement automated validation checks to improve data accuracy and reduce reporting time.
• Optimize data workflows for performance, scalability, maintainability, and cost efficiency.
• Monitor and troubleshoot pipelines in production environments.
• Contribute across the full data engineering lifecycle, including solution design, architecture, estimation, sprint planning, development, testing, documentation, deployment, and operational follow-up.
• Work hands-on with Databricks, Apache Spark, Delta Lake, Azure services, and DevOps practices to deliver reusable and high-quality data solutions.

• Proven experience designing and implementing end-to-end data pipelines for both batch and streaming workloads.
• Hands-on expertise with Databricks, including Apache Spark, Delta Lake, job orchestration, performance tuning, and environment management.
• Strong knowledge of Microsoft Azure, particularly services used in data platforms such as Azure Data Lake Storage.
• Solid experience with DevOps practices, including source control such as Git, CI/CD pipelines, automated testing, environment promotion, and infrastructure as code.
• Strong understanding of data engineering guidelines, release processes, data validation, performance optimization, monitoring, and troubleshooting in production environments.
• Experience contributing in early solution design and throughout the full data engineering lifecycle.
• Ability to explain data architectures, pipelines, and technical trade-offs clearly to non-technical stakeholders.
• Fluent English.

Selections are made on an ongoing basis, so we recommend that you apply as soon as possible.