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Mid-level metro role with common data engineering skills; Azure specialization slightly reduces competition.
Azure Databricks and PySpark skills are transferable, but cloud-platform specialization moderately restricts industry fit.
Multiple mandatory Azure Databricks, PySpark, and 5+ years requirements make filters highly strict.
Design, build, and maintain scalable, robust data pipelines for large-scale structured and unstructured data on Azure.
Develop and optimize Spark jobs in Azure Databricks using PySpark and Spark SQL; implement data storage solutions using Azure Data Lake Storage Gen2 following medallion architecture.
Implement data security, automate deployments via CI/CD pipelines and Infrastructure as Code, and mentor junior data engineers for best practices.
5+ years of professional experience in data engineering with enterprise grade solutions.
3+ years hands-on experience with Azure data services including expert proficiency in Azure Databricks, Azure Data Lake Storage Gen2, Python, and PySpark.
Strong SQL skills and experience with data modeling concepts; experience with data pipeline orchestration tools like Azure Data Factory or Apache Airflow.
Work Location: Bangalore/Chennai; Employment Type: Full Time, 5 days work from office/ODC.
Experienced data engineer with deep expertise in Azure cloud data platforms and data pipeline development at scale.
Demonstrates strong technical ownership including mentoring, security implementation, automation (CI/CD, Terraform), and troubleshooting performance issues.
Comfortable collaborating with cross-functional teams (data architects, analysts, business stakeholders) translating business needs into data solutions.