- Translate business requirements into technical designs, delivery estimates, implementation tasks and acceptance criteria.
- Design, build and support Microsoft Fabric solutions using appropriate lakehouse, warehouse, orchestration and semantic-modelling patterns.
- Develop robust ingestion, ETL/ELT and integration pipelines using SQL, Python or PySpark as appropriate.
- Design and maintain dimensional and analytical data models, including star schemas, facts, dimensions and Power BI semantic models.
- Implement automated data-quality, validation, reconciliation and schema-change controls.
- Contribute to architecture decisions and lead detailed design sessions for assigned components or workstreams.
- Apply security and governance controls including least privilege, classification, lineage, metadata, retention and auditability.
- Use source control, peer review, automated testing, CI/CD and repeatable deployment practices.
- Create and maintain technical documentation including data flows, architecture views, configuration, support procedures and runbooks.
- Estimate and plan engineering work, manage dependencies and communicate delivery risks within Agile teams.
- Support testing, release, monitoring, troubleshooting, incident investigation and continuous improvement in production.
- Mentor junior engineers,



review technical work and promote reusable patterns and engineering standards.
- Communicate solution options, progress, risks and recommendations to technical and non-technical stakeholders.

Qualifications

Essential

- Demonstrable hands-on experience delivering data engineering solutions using Microsoft Fabric.
- Robust SQL development, troubleshooting and performance-optimisation skills.
- Practical experience with Python or PySpark, notebooks and distributed data-processing concepts.
- Experience building and operating ETL/ELT pipelines and integrating cloud, on-premises or third-party data sources.
- Experience with Fabric lakehouse and warehouse patterns, orchestration, OneLake concepts and Power BI semantic models, including Direct Lake where appropriate.
- Experience with data warehouses, data lakes and Azure data services such as Azure Data Lake Storage and Azure Data Factory.
- Strong knowledge of dimensional modelling, analytical structures and data-modelling trade-offs.
- Experience implementing unit, integration, reconciliation and regression testing for data solutions.
- Experience with Git, pull requests, source control, CI/CD and automated deploymen

📌 Senior Data Engineer (Bengaluru)
🏢 Version 1
📍 Bengaluru