Principal Data Engineer

Company Name :<\/b><\/span><\/span><\/span>
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Job Title :<\/b><\/span><\/span><\/span>
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Principal Data Engineer<\/span><\/span>
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Qualification :<\/b><\/span><\/span><\/span>
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Any graduation<\/span><\/span>
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Experience :<\/b><\/span><\/span><\/span>
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10+ Years<\/span><\/span>
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Must Have Skills :<\/b><\/span><\/span><\/span>
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• 10+ years of experience in data engineering and enterprise data platform development.<\/span><\/span>
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• Strong hands -on experience designing and implementing enterprise data pipelines and data platforms.<\/span><\/span>
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• Deep expertise in SQL, including complex joins, query optimization, and analytical workloads.<\/span><\/span>
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• Hands -on experience with SSIS, stored procedures, and database -level transformations.<\/span><\/span>
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• Strong understanding of data lakes, lakehouse architectures, and data warehouses.<\/span><\/span>
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• Experience working with structured, semi -structured, and unstructured data.<\/span><\/span>
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• Strong experience with Databricks and modern data engineering practices.<\/span><\/span>
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• Experience with Azure data services such as Azure Data Factory, Azure Synapse, and Azure Data Lake Storage, or equivalent cloud technologies.<\/span><\/span>
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• Experience implementing data quality, governance, and compliance frameworks in enterprise environments.<\/span><\/span>
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• Ability to design, build, troubleshoot, and optimize production -grade data systems independently.<\/span><\/span>
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• Strong collaboration and communication skills across engineering, analytics, ML, and business teams.<\/span><\/span>
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Good to Have Skills :<\/b><\/span><\/span><\/span>
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Experience with <\/span><\/span>streaming platforms<\/span><\/span><\/b> (Kafka, Spark Streaming, Flink).<\/span><\/span><\/span><\/span>
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• Knowledge of <\/span><\/span>DevOps and CI/CD<\/span><\/span><\/b> practices for data engineering.<\/span><\/span>
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  • Familiarity with <\/span><\/span>containerization and orchestration<\/span><\/span><\/b> (Docker, Kubernetes).<\/span><\/span>
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    • Exposure to <\/span><\/span>ML data pipelines<\/span><\/span><\/b> and feature engineering.<\/span><\/span>
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      • Relevant certifications in <\/span><\/span>cloud or data engineering<\/span><\/span><\/b>.<\/span><\/span>
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Roles and Responsibilities :<\/b><\/span><\/span><\/span>
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• Architect, build, and maintain end -to -end batch and streaming data pipelines for enterprise data platforms.<\/span><\/span>
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• Design and implement scalable data lake and lakehouse architectures to support analytics, reporting, and ML workloads.<\/span><\/span>
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• Develop and manage data ingestion frameworks from multiple source systems into centralized data platforms.<\/span><\/span>
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• Define and enforce enterprise data quality frameworks, including validation rules, reconciliation checks, and monitoring.<\/span><\/span>
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• Implement data governance practices, including metadata management, lineage tracking, and access controls.<\/span><\/span>
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• Design and optimize data transformation workflows using SQL, stored procedures, and ETL/ELT patterns.<\/span><\/span>
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• Modernize legacy ETL solutions into cloud -native, scalable data pipelines.<\/span><\/span>
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• Architect and support modern data services including Databricks, Azure Data Factory, Azure Synapse, and Azure Data Lake Storage.<\/span><\/span>
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• Optimize data models and performance for analytical and downstream consumption.<\/span><\/span>
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• Collaborate closely with business, analytics, and ML teams to translate data requirements into scalable data solutions.<\/span><\/span>
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• Provide technical leadership, set engineering standards, and mentor other data engineers as needed.<\/span><\/span>
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• Support production data platforms with a hands -on operational mindset, including troubleshooting and performance tuning.<\/span><\/span>
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Location :<\/b><\/span><\/span><\/span>
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Bangalore and Chennai<\/span><\/span>
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CTC Range :<\/b><\/span><\/span><\/span>
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25 LPA<\/span><\/span>
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Notice period :<\/b><\/span><\/span><\/span>
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Immediate<\/span><\/span>
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Shift Timings :<\/b><\/span><\/span><\/span>
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3pm -12pm/2pm -11pm<\/span><\/span><\/span>
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Mode of Interview :<\/b><\/span><\/span><\/span>
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Virtual<\/span><\/span>
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Mode of Work :<\/b><\/span><\/span><\/span>
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5 days work from office<\/span><\/span>
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Mode of Hire :<\/b><\/span><\/span><\/span>
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Permanent<\/span><\/span>
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Note :<\/b><\/span><\/span><\/span>
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