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Strong employer brand, metro location, and generalist mid-level data engineering profile increases candidate competition.
Core data engineering skills transfer well across industries, with Azure/Databricks specifics moderately limiting fit.
Explicit 6–8 years and many mandatory Azure, PySpark, CI/CD, and OpenShift skills make screening highly selective.
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Design, build, and optimize scalable ETL/ELT data pipelines using Python and PySpark within Azure Databricks and Azure Data Factory.
Implement and maintain monitoring, alerting, and dashboard solutions for data quality and pipeline performance using tools like Grafana.
Manage cloud infrastructure setup and CI/CD pipelines using OpenShift, HELM, GitHub Actions for automated testing, build, and deployment.
6-8 years of relevant data engineering experience.
Strong proficiency in Python, PySpark, and SQL for complex querying and ETL development.
Hands-on experience with Azure Data Factory, Azure Databricks, Azure SQL Server, Azure Key Vault, and OpenShift including HELM for deployments.
Work Experience Required: 6-8 years relevant experience.
Experienced in building and optimizing large-scale Azure cloud data platforms leveraging multiple Azure analytics services.
Skilled in creating CI/CD pipelines and containerized deployment environments with OpenShift and GitHub Actions.
Demonstrates expertise in data pipeline monitoring and performance tuning, with an understanding of data governance and security.