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Tier-1 brand, metro location, mid-level data engineer role, and broad skillset increase competition.
Requires core data engineering and Azure Databricks experience, moderately limiting cross-industry transferability.
Explicit 6–8 years plus mandatory Azure, PySpark, CI/CD and OpenShift skills create strict filters.
Design, build, and optimize scalable ETL/ELT data pipelines using Python and PySpark within Azure Databricks and Azure Data Factory.
Manage data ingestion from multiple sources and ensure data quality through testing and monitoring with tools like Grafana.
Handle cloud infrastructure setup and deployment on OpenShift using HELM, and implement CI/CD pipelines with GitHub Actions for automated testing and deployment.
6 to 8 years of relevant data engineering experience.
Proficiency in Python, PySpark, and SQL for large-scale data processing and complex querying.
Hands-on experience with Azure data services including Azure Databricks, Azure Data Factory, Azure SQL Server, and Azure Key Vault.
Experience with container orchestration (OpenShift) and CI/CD pipelines (GitHub Actions).
Experienced in designing and optimizing data pipelines focusing on performance tuning and scalable cloud data solutions on Azure.
Capable of managing end-to-end data platform infrastructure including containerized deployments and automated CI/CD.
Familiar with monitoring, observability, and data governance best practices to ensure high-quality data delivery.