Microsoft Fabric Developer / Data Engineer
EbizON (company site, opens in a new tab)3+ yrsOn-siteDirect career pageEst. 8–14 LPA
Posted on 12 Sept
Reach people at EbizON
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at EbizON you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, and maintain scalable data solutions using Microsoft Fabric components including Lakehouse, OneLake, Data Warehouse, and Data Factory.
- Build and optimize ETL/ELT pipelines, data transformation workflows with Python, PySpark, SQL, and Fabric Notebooks integrating multiple enterprise data sources.
- Implement and support deployment including CI/CD for Microsoft Fabric workloads, ensuring data quality, validation, and performance optimization.
Minimum Requirements
- 3+ years of Data Engineering experience with Azure Data Platform and Microsoft Fabric.
- Hands-on expertise with Fabric Data Factory, Pipelines, Dataflows Gen2, OneLake, Fabric Lakehouse, Data Warehouse, and Notebooks.
- Proficient in SQL/T-SQL, Python, PySpark, ETL/ELT development, data modelling, and integration of REST APIs.
- Familiarity with Azure ecosystem components like Azure Data Factory, Azure SQL, ADLS, Azure Synapse and experience with Delta Lake and Medallion Architecture.
Ideal Candidate Profile
- Has practical experience building and deploying Microsoft Fabric solutions end-to-end, beyond certifications or theory.
- Comfortable explaining architecture, pipelines, transformations, and Fabric components they personally implemented.
- Experienced in integrating multiple data sources, optimizing data models for analytics, and supporting enterprise-scale data platforms.