Data Platform Engineer

Job Description Be part of something bigger. Decode the future.  At Electrolux, as a leading global appliance company, we strive every day to shape living for the better for our consumers, our people and our planet. We share ideas and collaborate so that together, we can develop solutions that deliver enjoyable and sustainable living. Come join us as you are. We believe diverse perspectives make us stronger and more innovative. In our global community of people from 100+ countries, we listen to each other, actively contribute and grow together. Join us in our exciting quest to build the future home. All about the role: As a Data Engineer at Electrolux Global Data & AI (GDAI) Team, you are the backbone of our digital supply chain for information - the person who turns raw, scattered data into reliable and trusted data and AI products. You will work across the full lifecycle of data, from ingestion through transformation to delivery, on our modern Lakehouse stack built on Azure Databricks. Like the Data Engineering teams at the world's largest tech companies, we hold a high bar: we hire builders who think like owners, dive deep into ambiguous problems, and are equally comfortable architecting a pipeline as they are explaining a metric to a business stakeholder. You will have broad exposure across ingestion, platform, modeling, and delivery, and you will help define the boundary between our Platform and Product Data Engineering functions as our data organization scales. What you’ll do: Data Ingestion & Integration: Design and build pipelines that reliably move data from a wide variety of source systems – relational databases, external sources and operational datastores - into our Lakehouse, merging disparate sources into a single, trustworthy picture for the business. Transformation & Modeling: Build and maintain ELT pipelines using dbt to transform raw data into clean, well-modeled, business-ready assets, applying dimensional modeling and consistent KPI definitions so the same metric never means two different things in two different dashboards. Platform Contribution: Operate and extend our Azure Databricks Lakehouse, contributing to schema design, query optimization, and scalable data processing so the platform scales reliably as data volume and complexity grow. Orchestration & Reliability: Own end-to-end pipeline orchestration in Airflow, building workflows that run in the right order, recover automatically from failure, and are observable through monitoring, alerting, and audit trails. Data Quality & Trust: Implement automated data quality checks and testing so that accuracy, completeness, and timeliness are verified continuously, not discovered by a business user staring at a broken dashboard. Data Mesh & Domain Ownership: Apply data mesh principles to design data products with clear ownership, discoverability, and contracts across domains, reducing tight coupling between teams and enabling other domains to self-serve trusted data.