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Tier-1 employer, Bangalore metro, and 4–8 year mid-level role increase competition.
Azure-specific data engineering skills (Databricks, Synapse, KQL) make cross-industry transfer moderately constrained.
Explicit 4–8 years plus many mandatory Azure/Databricks/Scala/Spark technologies make filters strict.
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Develop and maintain complex data queries and pipelines using Kusto Query Language (KQL), SQL, Databricks, Spark SQL, PySpark, and Scala for large-scale Azure cloud data platforms.
Administer and optimize data engineering components including Databricks workspace, Azure Synapse pipelines, and CI/CD pipelines with automation tools like Ansible.
Manage deployment scripts, infrastructure automation, and data orchestration to ensure performance tuning, troubleshooting, and scalability of ETL/ELT frameworks.
4–8 years of experience in Data Engineering, Analytics Engineering, or related field.
Strong proficiency in Kusto Query Language (KQL), SQL, Databricks, Scala, Spark SQL, PySpark, Azure Synapse Analytics, Azure DevOps, Ansible, and CI/CD pipeline management.
Experience with administration, configuration, monitoring, and optimization of Databricks workspace and Synapse pipelines.
Work Experience Required: 4–8 years in relevant Azure-based data platform and big data technologies.
Experienced data engineer familiar with large-scale Microsoft Azure cloud data platforms and ETL/ELT best practices.
Hands-on expertise in building, optimizing, and troubleshooting scalable data transformation pipelines with a focus on automation and performance tuning.
Comfortable working within Agile/Scrum and DevOps environments involving CI/CD pipelines and infrastructure-as-code deployment.