Microsoft Fabric

LTM; Hyderabad; On-site; INR 1,500,000 - 2,800,000; Full time

Job description

Key Responsibilities

• Design and deliver endtoend data solutions using Microsoft Fabric including OneLake Lakehouse Data Pipelines Fabric Notebooks and Power BI semantic models


• Build scalable batch incremental and nearrealtime ingestion pipelines from relational filebased API and eventdriven sources


• Develop reusable transformations using Python PySpark and SQL with modular patterns for data cleansing enrichment reconciliation and businessrule processing


• Implement medallionstyle data architecture using Bronze Silver and Gold layers Delta tables and analyticsready curated datasets


• Design dimensional models fact and dimension tables semantic models and governed business metrics for reporting and analytics


• Configure data quality checks schema validation deduplication replay backfill and errorhandling patterns to improve pipeline reliability


• Implement security and access controls using Microsoft Entra ID RBAC service principals workspace roles leastprivilege principles and appropriate sensitivedata controls


• Enable metadata management lineage classification and governance in collaboration with enterprise data governance and security teams


• Create CICD and sourcecontrol practices for Fabric artifacts and related data engineering assets using Git or GitHub Actions


• Monitor pipeline health troubleshoot production failures perform root cause analysis and tune workloads for performance capacity and cost efficiency


• Partner with architects analysts product owners and business stakeholders to translate requirements into secure supportable and welldocumented data products


• Contribute to coding standards design reviews peer reviews reusable patterns technical documentation and knowledge transfer

Required Qualifications

• 3 years of experience in data engineering data platforms analytics engineering or a closely related role


• Demonstrated handson experience delivering production solutions with Microsoft Fabric


• Strong practical knowledge of OneLake Lakehouse Data Pipelines Fabric Notebooks and Power BI semantic models


• Advanced development skills in Python PySpark and SQL including performance tuning and troubleshooting


• Experience designing ETLELT pipelines Delta Lake or equivalent lakehouse patterns and dimensional data models


• Experience with data quality observability logging ing and production support for enterprise data pipelines


• Experience implementing identity access control secrets management and secure servicetoservice connectivity in Azure environments


• Working knowledge of Gitbased version control and automated deployment practices for data platform assets


• Strong communication skills and the ability to work effectively with technical and business stakeholders

Preferred Qualifications

• Microsoft Certified Fabric Data Engineer Associate or an equivalent Microsoft data certification


• Experience with ADLS Gen2 or Azure streaming services


• Experience with Delta Lake Kafka Event Hubs Fivetran or comparable ingestion and transformation technologies


• Experience implementing data cataloging lineage classification privacy controls and regulatory datagovernance requirements


• Experience with Terraform and broader DevSecOps practices


• Exposure to crosscloud data platforms such as Snowflake and AWS is beneficial


• Experience coaching engineers conducting design reviews and defining reusable engineering standards

Technical Competency Profile

• AreaExpected capability


• Core platformMicrosoft Fabric OneLake Lakehouse Data Pipelines Fabric Notebooks


• EngineeringPython PySpark SQL ETLELT batch and streaming ingestion


• AnalyticsPower BI Semantic models dimensional modeling governed business metrics


• Azure ecosystemADLS Gen2 Event Hubs


• Security governanceMicrosoft Entra ID RBAC service principals workspace roles lineage classification


• DevOps operationsGit GitHub Actions CICD monitoring ing RCA performance tuning

Success Measures

• Reliable and timely delivery of secure analyticsready datasets and data products


• Improved pipeline stability observability recoverability and adherence to agreed service expectations


• Reusable engineering patterns effective environment promotion and reduced manual deployment effort


• Measurable improvements in workload performance data quality governance and platform cost efficiency


• Clear documentation effective stakeholder communication and successful knowledge transfer