How much salary can I expect as a Azure Databricks Data Engineer in Infosys in Bengaluru/Bangalore?

You can expect a minimum salary of 0 INR. The salary offered will depend on your skills, experience and performance in the interview.

What is the eligibility criteria to apply for Azure Databricks Data Engineer in Infosys in Bengaluru/Bangalore?

The candidate should have completed the required education and people who have 5 to 9 years are eligible to apply for this job. You can apply for more jobs in Bengaluru/Bangalore to get hired quickly.

Is there any specific skill required for this job?

The candidate should have sound communication skills and sound communication skills for this job.

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Both Male and Female candidates can apply for this job.

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Azure Databricks Data Engineer

Infosys

Azure Databricks Data Engineer

Infosys

Bengaluru/Bangalore

Not disclosed

Work from Office

Full Time

Min. 5 years

Job Description

IT Consulting

• Primary skills:Technology->Data On Cloud - Platform->Azure Data Lake (ADL) 5–9 years of overall experience in data engineering / big data development 3+ years of hands-on experience with Azure Databricks Strong expertise in PySpark / Spark SQL Experience with Microsoft Azure services, including: Azure Data Factory (ADF) Azure Data Lake Storage (ADLS) Azure SQL / Synapse (good to have) Solid understanding of data lake / lakehouse architectures Strong SQL skills for data transformation and analytics Experience with workflow orchestration and scheduling Knowledge of data warehousing and data modeling concepts Familiarity with Git and CI/CD pipelines Excellent problem-solving and communication skills Databricks Certified Data Engineer (Associate / Professional) Experience with Delta Lake Knowledge of streaming frameworks (Structured Streaming, Event Hubs) Exposure to BI tools (Power BI, Tableau) Experience working in Agile / Scrum teams Basic understanding of Python best practices and libraries Design, develop, and optimize data pipelines using Azure Databricks (PySpark / Spark SQL) Build and manage scalable data ingestion, transformation, and processing workflows Implement ETL/ELT pipelines using Databricks, Azure Data Factory, and related tools Develop data solutions using Azure Data Lake Storage (ADLS Gen2) Optimize Spark jobs for performance, cost, and scalability Implement best practices for data modeling, partitioning, and schema management Work with structured, semi-structured, and unstructured data Collaborate with business, analytics, and downstream BI teams Ensure data quality, reliability, and monitoring of data pipelines Support CI/CD, version control, and automated deployments