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Metro Bangalore location and common junior DataOps skillset increase applicant competition moderately.
Azure Databricks and ADF skills are transferable across industries but require cloud-specific experience.
Specific Azure Databricks, ADF, PySpark requirements and an explicit 1–3 years mandate create strict screening filters.
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Operate and troubleshoot data pipelines using Azure Data Factory and Databricks to ensure reliable data flow into Delta Lake.
Develop and maintain automated data transformations using Spark SQL and PySpark in Databricks notebooks, focusing on data quality and structured datasets.
Collaborate with senior engineers and stakeholders to translate business requirements into technical solutions and maintain documentation for data models and workflows.
1–3 years of professional experience including internships or academic projects with cloud data services.
Proficiency in SQL (complex querying) and Python/PySpark for data transformation.
Hands-on experience with Azure Databricks, Azure Data Lake Storage (ADLS), and Azure Data Factory.
Bachelor’s degree in Computer Science, Data Science, Statistics, or a related quantitative field; certifications like Microsoft Certified: Azure Data Engineer Associate are preferred but not mandatory.
Experience in data pipeline operations within Azure cloud environments ensuring automation, reliability, and security.
Capable of troubleshooting data jobs, conducting root-cause analysis, and collaborating across functions for technical and change management documentation.
Exposure to CI/CD workflows using Azure DevOps and version control using Git.