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Tier-1 brand and metro location increase applicant density, though Databricks specialization moderates competition.
Medium — Databricks and data engineering skills transfer across industries but require platform-specific expertise.
Explicit 7–12 year band plus numerous mandatory Databricks, cloud, and DevOps skills creates high screening strictness.
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Design, develop, and maintain scalable data engineering solutions on the Databricks platform using PySpark and SQL.
Implement and optimize data architectures, pipelines, and processing workflows incorporating Databricks features such as Delta Lake, Delta Live Tables, Unity Catalog, and Serverless Compute.
Collaborate with stakeholders to translate business requirements into technical solutions and lead initiatives on performance and cost optimization.
7-12 years total work experience with at least 5 years in relevant data engineering roles.
Strong hands-on experience with Databricks, PySpark, SQL, and cloud platforms (Azure, AWS, or GCP).
Experience with Delta Lake, Delta Live Tables, Unity Catalog/Data Governance, Databricks Workflows, and Serverless Compute.
Bachelor's or master's degree in any field.
Experienced with large-scale data platforms and modern data architectures such as Lakehouse and Medallion frameworks.
Proficient in DevOps practices, CI/CD pipelines, Agile delivery, and cloud-native services across Azure, AWS, or GCP.
Capable of managing end-to-end data engineering lifecycle including design, implementation, testing, troubleshooting, and documentation in enterprise environments.