Data Engineer

As a Data Engineer on the EDP team within Nokia’s Digital Office and Enterprise Chief Data Office, you will help build a modern data-as-a-service platform that combines platform engineering, pipeline development, and analytics enablement. The role spans scalable data ingestion, transformation, and delivery across a growing and evolving platform. You will join a global team working across multiple countries and collaborate closely with architects, technical leads, product owners, fellow data engineers, and broader IT partners. This is an individual contributor role with a strong emphasis on teamwork, learning, and shared ownership of the platform. The platform sits at the forefront of cloud data engineering, giving you exposure to evolving Databricks and Azure capabilities as they are adopted and put into practice. You will also work alongside specialist partners — including platform vendors, independent software Build, maintain, and improve ingestion pipelines, notebooks, and data assets on Azure and Databricks using Delta Lake and PySpark, supporting reliable data delivery across the platform. Develop and optimize SQL and Python solutions that improve pipeline performance, data quality, and platform stability. Partner with architects, leads, product owners, and engineering peers to understand requirements, troubleshoot issues, and deliver practical data solutions. Provide specialist platform support to the operations team by diagnosing pipeline and platform issues, sharing deep technical knowledge, and guiding effective resolutions. Contribute to Power BI reporting and analytics enablement, turning curated platform data into useful insights for stakeholders. Use CI/CD practices in Azure DevOps to test, deploy, and maintain platform changes safely and efficiently. Help evaluate and apply new platform capabilities in partnership with internal architects and external specialists, turning emerging features into reliable engineering patterns for the team. Apply AI-assisted tools daily for research, design, coding, testing, and documentation, using them to move faster while maintaining engineering quality. What success looks like in your first year You are confident and effective working across the EDP platform stack and team ways of working. You are a trusted specialist partner to the operations team, helping them resolve platform and pipeline challenges with clear technical guidance. You are delivering ingestion pipelines, notebooks, and analytics-ready outputs that meet team and stakeholder needs. You are contributing to platform optimization and evolution, including practical use of newer platform capabilities and patterns shaped with architects and external partners. Must-have Strong fundamentals in SQL, coding, and problem-solving, with the ability to break down technical challenges and learn new tools quickly. Hands-on experience building or supporting data pipelines or data platform components in cloud or distributed data environments. Comfort working with cloud data engineering tools, including experience with or willingness to work deeply in Azure, Databricks, Delta Lake, and PySpark. Experience with collaborative software development — version control, code review, and working effectively in cross-functional engineering teams. Clear communication skills and a proactive mindset, with confidence collaborating across time zones and using AI-assisted development tools as part of your daily workflow. Nice-to-have Experience with Azure DevOps for CI/CD, testing, and deployment of data or platform workloads. Exposure to Power BI or similar analytics/reporting tools for delivering data to business users. Familiarity with lakehouse architecture, distributed processing with Spark, and platform performance tuning. Interest in working with new or preview platform features, and in collaborating with vendor specialists, ISVs, or architecture partners on complex technical challenges. Background in Agile delivery, mentoring or knowledge sharing, or personal and project work that demonstrates curiosity — including experimentation with AI tools, algorithms, or modern development workflows.