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Tier-1 employer, metro location, and a popular data-engineer title increase candidate competition significantly.
Platform-specific skills (Azure/Databricks) are transferable but require specific tooling experience, giving moderate sensitivity.
Many mandatory platform and infra skills (Databricks, PySpark, Azure, Terraform, Kubernetes) enforce strict filters.
Design, build, and manage end-to-end scalable data pipelines and ELT flows using Azure services including Databricks (PySpark), ADLS Gen2, ADF, and Synapse Analytics.
Administer and secure Azure databases, implement data modeling (star/snowflake schemas) and data architecture strategies involving data lakes and warehouses.
Operate DevOps CI/CD pipelines and infrastructure as code (Terraform), and leverage Linux, Docker, and Kubernetes in the data engineering workflow.
Proven experience delivering complex data products with expertise in Azure data services: Databricks (PySpark), ADLS, ADF, SQL, and Python.
Experience with DevOps CI/CD practices, GIT version control, and infrastructure as code tools like Terraform.
Familiarity with Linux, Docker, and Kubernetes environments.
Work Experience Required: Not explicitly mentioned in the JD.
Strong background in cloud-based data engineering focusing on Microsoft Azure ecosystem and big data technologies (Spark).
Experience handling all stages of data pipelines from integration, transformation to secure storage within enterprise-scale environments.
Comfortable operating in DevOps-focused environments incorporating CI/CD workflows and infrastructure automation tools.