Sr. Data Platform Engineer – Azure & Microsoft Fabric
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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level data platform role in Mumbai with common skills and moderate brand, attracting many qualified applicants.
Core data engineering skills are transferable, though Microsoft Fabric/Synapse specialization narrows cross-industry fit.
Explicit 5–8 years requirement plus Fabric/Synapse and strong ETL/SQL mandates make filters stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable data pipelines and ETL/ELT processes in Microsoft Fabric, including Data Factory pipelines, Dataflows, and Notebooks, supporting a Medallion Architecture within OneLake.
Lead the migration of data workloads from Azure Synapse Analytics to Microsoft Fabric, ensuring data quality, consistency, and minimal disruption.
Support light DevOps activities (~10%) such as CI/CD deployment, environment management, and release coordination for Fabric artifacts.
Minimum Requirements
5–8 years of experience in data engineering roles with strong ETL/ELT expertise.
Hands-on experience with Microsoft Fabric (Data Factory pipelines, OneLake) and Azure Synapse Analytics, including involvement in Synapse-to-Fabric migration preferred.
Advanced SQL skills including query optimization, stored procedures, and performance tuning.
Experience integrating data from REST/SOAP APIs, databases, and third-party sources into pipelines; working knowledge of Power BI and basic CI/CD practices (Azure DevOps or Git).
Ideal Candidate Profile
Demonstrated expertise operating in modern data platform environments implementing Medallion Architecture and managing large data ingestion and transformation pipelines with Microsoft Fabric and Azure Synapse.
Experience driving complex migration projects from legacy cloud data platforms (Azure Synapse) to new platforms (Microsoft Fabric), balancing technical execution and stakeholder collaboration.
Proficient in collaborating with BI developers and business stakeholders to align data engineering solutions with downstream analytics, emphasizing performance, reliability, and governance.