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2 days ago

bangalore, India Tata Consultancy Services Full time

Greetings from TCS Job Title: Senior Azure Data Engineer Required Skillset: Azure data factory, data bricks, data lake, automation and performance optimization of ETL. Location: Gurugram Experience Range: 6-8 years Job Description Must-Have** Good Knowledge of Data Brick lakehouse and Azure DataLake concept · Knowledge of Data Bricks delta concept Delta live tables (DLT) · Strong hands-on experience in ELT– pipeline development using Azure Data factory and Databricks Autoloader, Notebook scripting and Azure Synapse Activity Copy, Data Flow Task · Strong knowledge of metadata-driven data pipeline, metadata management, dynamic logic · In-depth knowledge of data storage solutions, including Azure Data Lake Storage (ADLS), and Azure Serverless SQL Pool. · Experience with data transformation using Spark, and SQL technologies. · Solid understanding of design patterns, and best practices of the cloud stack. · Experience with code management and version control using Git or similar tools. · Strong problem-solving and debugging skills in ETL workflows and data pipelines. · Strong understanding of Azure Data bricks and Azure Synapse internals – features and capabilities. Knowledge of Azure DevOps and continuous integration and deployment (CI/CD) process. · Knowledge of data quality and data profiling techniques, with experience in data validation and data cleansing Good To Have: Power Shell Knowledge Power BI Knowledge Responsibility of / Expectations from the Role: Conducting technical sessions, design reviews, code reviews, and demos of pipelines and their functionality Developing technical specification for Data pipelines and workflow and getting sign-off from Architect and Stryker leads. Developing, deploying, and maintaining workflows and data pipelines using Azure Data bricks and Azure Synapse. Developing pipeline/notebook for Delta live tables ( DLT) Databricks Auto Loader, Notebook scripting and Azure Synapse Activity Copy, Data Flow Task Writing efficient and high-performing ETL code using PySpark, and SQL technologies. Thanks & Regards, Ria Aarthi A.

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