Senior Data Engineer-1
electrolux · Bangalore ·
- Seniority
- Senior
- Employment
- Full time
- Category
- Data engineering
- Experience
- 3+ years
- Company size
- 1000+
electrolux · Bangalore ·
Sytac · Netherlands
issgovernance · Gurgaon, India
DATAPAO · Spain
The Senior Data Engineer will build and maintain scalable data pipelines and Lakehouse architecture on Azure Databricks to support Electrolux's global data and AI initiatives. The role involves end-to-end data lifecycle management, including ingestion, transformation using dbt, orchestration with Airflow, and implementing data quality standards.
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.
Automation & Engineering Rigor: Bring a software engineering mindset to Data Engineering - version-controlled code, CI/CD, code review, and Infrastructure as Code - so pipelines are tested, repeatable, and maintainable rather than one-off scripts.
Cross-Functional Partnership: Work directly with business stakeholders to translate ambiguous requirements into scalable data solutions and collaborate closely with the Platform and AI teams to align on shared infrastructure, standards, and roadmap, communicating trade-offs clearly to both technical and non-technical audiences.
Cost & Performance Ownership: Monitor and optimize computing and storage costs on Azure, treating cost efficiency as a first-class engineering concern rather than an afterthought.
AI-Assisted Engineering: Use AI pair-programming tools such as GitHub Copilot to accelerate development and build agentic CI/CD workflows in GitHub Actions that automate testing, review, and deployment with minimal manual intervention.
Who are you
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.
Technical Qualifications:
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.
This is a full-time position, based in Bangalore, India.
Why work for Electrolux?
Build at scale: Lead the development of mission-critical digital commerce experiences used by thousands of customers daily.
Own the architecture: Influence core technical decisions and help evolve our platform for high scalability, resiliency, and flexibility.
Modern tech stack: Work with cutting-edge tools in cloud, commerce, and full-stack engineering.
Empowered leadership: Drive strategy and execution while mentoring a world-class engineering team.
Growth and impact: Join a high-growth company with a culture that values engineering excellence, innovation, and transparent leadership.
As part of the Electrolux Group, we will continuously invest in you and your development. There are no barriers to where your career could take you!