Senior Data Engineer-1

 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.

• Proven Track Record: 3+ years of experience as a Data Engineer building and operating production data pipelines at scale, ideally in a fast-paced, high-growth environment like those found at leading tech companies.

• SQL & Python: Strong hands-on skills in SQL (query optimization, schema design) and Python for pipeline development and automation.

• Cloud & Lakehouse Experience: Practical experience with a cloud data platform (Azure strongly preferred) and modern Lakehouse or data warehouse architecture.

• Pipeline & Transformation Tooling: Experience building ELT/ETL pipelines, ideally with dbt, and orchestrating them with a workflow manager such as Airflow.

• Ownership Mindset: A track record of taking ambiguous problems from first principles to a working, reliable solution, and a comfort level moving across ingestion, modeling, and delivery as the situation demands.

• Communication: Ability to explain technical trade-offs to non-technical stakeholders and translate business requirements into scalable, well-modeled data assets.

• AI-Assisted Engineering: Hands-on experience using GitHub Copilot or similar AI coding assistants in daily development, and building CI/CD pipelines in GitHub Actions.

Our data platform is built around a modern Lakehouse architecture. You will work with:

• Core Platform: Azure Databricks Lakehouse on Microsoft Azure.

• Transformation & Modeling: dbt (data build tool) for version-controlled, testable data modeling.

• Orchestration: Apache Airflow for workflow scheduling, dependency management, and recovery.

• Ingestion: Azure Data Factory and batch/streaming integration patterns.

• Languages: Python and SQL at a professional, production-grade level.

• Architecture: Data Mesh principles applied to domain-oriented data ownership and data products.

• Engineering Practices: GitHub and GitHub Actions for version control and CI/CD, agentic CI/CD workflows, and Infrastructure as Code.

• AI-Assisted Development: GitHub Copilot and similar AI pair-programming tools embedded in the daily workflow.