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Mid-level data engineering role in Bangalore with a generalist title and metro location increases candidate competition.
Azure-centric data engineering skills transferable to many industries but some cloud-platform specificity increases domain sensitivity.
Explicit 5–8 years requirement plus mandatory Azure data stack, data modelling, and DataOps filters candidates strictly.
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Design, develop, and optimize scalable data pipelines and platforms using Azure Synapse Analytics, Azure Data Factory, Azure Data Lake, and related Azure data services.
Build high-performance solutions leveraging SQL and Python, focusing on scalability, maintainability, and query optimization for enterprise-scale datasets.
Collaborate directly with clients and stakeholders to gather requirements, design solutions, provide technical guidance, and troubleshoot production issues including performance optimization.
5–8 years of experience in Data Engineering with strong expertise in enterprise cloud data platforms.
Proficient in SQL, Python, Azure Synapse Analytics, Azure Data Factory, Azure Data Lake; hands-on experience with Microsoft Azure architecture and cloud-native data engineering practices.
Bachelor's degree in Computer Science, Information Technology, Data Engineering, or related field.
Experience working directly with clients and stakeholders in gathering requirements and delivering technical solutions.
Experienced in data modelling including dimensional modelling, star/snowflake schemas, and Slowly Changing Dimensions (SCD) for data warehouse design.
Familiar with implementing CI/CD pipelines and DataOps best practices for automated deployment, testing, and monitoring.
Demonstrates ability to optimize data workloads on cloud platforms including partitioning, indexing, storage, versioning, and compute cost optimization for large-scale enterprise data environments.