Azure Databricks Data Engineer – Apache Spark & Azure Data Factory
Synechron (company site, opens in a new tab)10+ yrsPuneOn-siteDirect career pageEst. 22–35 LPA
Posted 15h ago
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, and maintain cloud-based ETL/ELT data pipelines using Azure Databricks, Apache Spark, and Azure Data Factory.
- Optimize Databricks clusters and Spark workloads for performance and cost-efficiency, including tuning caching, queries, and resource usage.
- Monitor, secure, and automate data platforms including credential management with Azure Key Vault and service principals, and resolve production issues promptly.
Minimum Requirements
- 10+ years of experience in data engineering or Azure cloud technologies with 3+ years focused on optimizing Databricks workloads.
- Proficient in Azure services (Databricks, Data Factory, Data Lake, Key Vault), Apache Spark, PySpark, Python, T-SQL, and Linux/Shell scripting.
- Bachelor’s or master’s degree in Computer Science, IT, Mathematics, Data Engineering or related field; equivalent experience considered.
- Certifications in Azure or Databricks preferred; completion of Synechron security, compliance, and project training required.
Ideal Candidate Profile
- Experienced in building and operating enterprise-scale, secure data platforms with a focus on cloud cost and resource optimization.
- Strong practical skills in data pipeline architecture, performance tuning, and automation in Azure and Databricks environments.
- Familiar with Agile delivery methodologies, CI/CD pipelines, and collaborating effectively with cross-functional engineering and stakeholder teams.