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Bangalore mid-level data engineer role with common skillset and known employer increases candidate competition.
Specialized Azure Databricks and PySpark requirements make cross-industry fit moderately restrictive.
Explicit 6–8 years plus specific Azure, Databricks, PySpark, OpenShift, and CI/CD requirements raise strictness.
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Design, build, and optimize scalable ETL/ELT data pipelines primarily using Python and PySpark on Azure Databricks and Azure Data Factory.
Develop and maintain data ingestion, processing workflows, and data quality monitoring solutions including unit/integration tests and Grafana dashboards.
Manage cloud data infrastructure on OpenShift with HELM, implement CI/CD pipelines via GitHub Actions, and optimize performance of data pipelines and databases.
6–8 years of relevant experience in data engineering or related roles.
Strong proficiency in Python, PySpark, and complex SQL for ETL and data processing.
Hands-on experience with Azure data services: Azure Databricks, Azure Data Factory, Azure SQL Server, Azure Key Vault.
Experience with OpenShift container management, HELM, CI/CD (GitHub Actions), and monitoring tools like Grafana.
Experienced in architecting and operationalizing large-scale data pipelines and cloud infrastructure in Azure and OpenShift environments.
Skilled in automation and DevOps best practices, ensuring robust CI/CD and observability for data engineering projects.
Strong technical background in SQL optimization, data modeling, and Azure analytics services with certifications preferred.