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Tier-1 brand and metro location increase competition, but niche certifications and seniority narrow applicant pool.
Specialized data-engineering platforms and mandatory certifications reduce cross-industry transferability.
Mandatory Databricks/Azure certifications, specific tech stack, and explicit 8-12 years requirement make filters strict.
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Lead design and development of data infrastructure, pipelines, and integration solutions leveraging Azure-based technologies and advanced data engineering techniques.
Develop and implement complex PySpark-based data analysis and transformation scripts to drive actionable business insights.
Manage troubleshooting, debugging, and deployment workflows including source control (GitHub, Azure DevOps) and CI/CD pipelines for data solutions.
8-12 years total work experience with minimum 5 years relevant to data engineering at architect/managerial level.
Mandatory skills: Azure Databricks, PySpark, Azure Data Factory, Azure Fabric, SQL Server (complex stored procedures), Python scripting mandatory.
Required certifications: Databricks Certified Data Engineer Associate/Professional AND DP600 (Azure Fabric Analytics Engineer Associate) OR DP700 (Azure Fabric Data Engineer Associate).
Bachelor's degree in Engineering (B.Tech/B.E)/M.Tech/M.E/MCA required.
Experienced data engineering professional with a strong background in Azure cloud analytics and big data platforms at senior/managerial level.
Hands-on expertise in scripting, data pipeline architecture, and operationalizing data solutions using Azure and Databricks ecosystems.
Capable of managing end-to-end data engineering workflows, emphasizing troubleshooting and deployment automation.