Job Description
Job Description
Azure Data Engineer - Immediate Joiners Only
\nYears of Experience - Min 3 and Max upto 7
\nBangalore / Pune/ Mumbai/ Gurgaon
\nJob Description
\nLooking for a hands‐on Senior Data Engineer – Azure & Databricks with up to 6 years of total experience to build, optimize, and maintain scalable cloud data platforms.
\nThis is an individual contributor role, focused on developing reliable data pipelines and analytics‐ready datasets using Microsoft Azure and Databricks. The role requires strong hands‐on expertise in SQL and Python, along with experience building batch (and basic streaming) data pipelines in a cloud environment.
\nYou will collaborate closely with architects, analytics teams, QA, DevOps, and business stakeholders in a global delivery model.
\nMust have skills
\nHands‐on experience delivering Azure‐based data engineering solutions
\nCloud & Data Engineering (Azure)
\nStrong hands‐on experience with Azure data services, including:
\nAzure Data Lake Storage Gen2 (ADLS)
\nAzure Data Factory (ADF)
\nAzure Databricks
\nAzure Synapse Analytics
\nExperience designing cloud‐native data lakes and analytical data stores
\nSolid understanding of batch data pipelines and basic streaming concepts
\nSQL & Python (Mandatory)
\nStrong SQL skills (mandatory)
\nWriting complex queries, joins, aggregations, and transformations
\nHands‐on experience working with large datasets in Synapse / Databricks
\nStrong Python skills (mandatory)
\nPython for ETL / ELT and data engineering use cases
\nHands‐on experience with PySpark in Databricks
\nStrong understanding of data modeling, transformations, and query performance tuning
\nData Processing & Engineering
\nHands‐on experience with Spark / PySpark on Databricks
\nExperience handling structured and semi‐structured data
\nUnderstanding of partitioning, schema evolution, and data validation concepts
\nDevOps & Platform Basics
\nWorking knowledge of Infrastructure as Code (ARM Templates and/or Terraform)
\nBasic experience with CI/CD pipelines (Azure DevOps or GitHub Actions)
\nUnderstanding of logging and monitoring using Azure Monitor / Log Analytics
\nCollaboration
\nAbility to work effectively with architects, QA, DevOps, and business stakeholders
\nGood communication skills for explaining technical concepts clearly
\nGood to have skills
\nExposure to streaming technologies such as Azure Event Hubs or Kafka
\nFamiliarity with Lakehouse architecture and Delta Lake concepts
\nExperience integrating Azure data platforms with BI tools (Power BI preferred)
\nBasic knowledge of data governance and data quality frameworks
\nAwareness of Azure cost‐optimization best practices
\nExperience working in Agile delivery models, especially with global clients
\nKey responsibiltes
\nData Engineering & Development
\nBuild scalable ETL / ELT pipelines using Azure and Databricks
\nDevelop SQL‐based transformations and Python / PySpark pipelines
\nIngest and process data using ADF, Databricks, and ADLS
\nBuild analytics‐ready data models optimized for performance and cost
\nPlatform & Operations
\nSupport deployment and execution of data pipelines across environments
\nMonitor data pipeline health, performance, and data quality
\nTroubleshoot pipeline failures and perform root‐cause analysis
\nFollow Azure security, reliability, and scalability best practices
\nCollaboration & Delivery
\nWork closely with architects and product teams to understand requirements
\nTranslate business and analytics needs into working Azure data solutions
\nContribute to documentation, code reviews, and engineering standards
\nEducation Qulification
\n1. Bachelor's or Master Degree or equivalent Degree
\nCertification If Any
\n1. Microsoft Certified: Fabric Data Engineer Associate
\n2.Microsoft Certified: Azure Solutions Architect Expert
\n3. Snowflake Core
\nShift timing
\n12 PM to 9 PM and / or 2 PM to 11 PM - IST time zone
\nShift timing
\n12 PM to 9 PM and / or 2 PM to 11 PM - IST time zone