Omkar

1. Design, develop, and manage data pipelines for ETL processes, ensuring data integration and transformation.
2. Implement data lake storage solutions, organize data hierarchies, and control access for efficient data storage and retrieval.
3. Create real-time data processing solutions, handling high-velocity data streams with low-latency processing.
4. Develop and optimize big data queries using U-SQL for data analysis and insights generation.
5. Establish and maintain a centralized metadata catalogue to discover and understand data assets.
6. Collaborate with stakeholders by securely sharing datasets and insights across Azure subscriptions.
7. Design and manage globally distributed, highly responsive NoSQL databases for scalable applications
8. Set up event ingestion and processing solutions for real-time analytics and telemetry data.
9. Create workflow automation for data integration and movement across various services.
10. Develop interactive dashboards and reports for data visualization and business intelligence.
11. Leverage Java for developing custom data processing applications and services.
12. Utilize .NET languages like C# for building data-centric applications and services.
13. Implement and optimize Hadoop-based solutions for ample data storage and batch processing.
14. Manage NoSQL databases for unstructured and semi-structured data storage and retrieval.
15. Set up Kafka clusters and implement data streaming solutions for real-time data processing.
16. Collaborate effectively with version control systems to manage code changes and repositories.
17. Automate the build and deployment processes for data engineering solutions.
18. Implement DevOps principles for infrastructure as code, automated testing, and continuous delivery in data engineering projects.
19. Design and automate end-to-end data pipelines, ensuring data quality and reliability.
20. Cleanse and transform data from various sources to prepare it for analysis and reporting.
21. Create and maintain data models and schemas for efficient storage and retrieval.
22. To protect sensitive information, Implement data security measures, including encryption and access controls.
23. Optimize data processing performance by fine-tuning configurations and queries.
24. Develop robust error handling and logging mechanisms for data pipelines.
25. Maintain comprehensive documentation for data engineering solutions, including architecture diagrams and data lineage.
26. Set up monitoring tools and alerts to identify and address data pipeline issues proactively.
27. Optimize resource usage in Azure to control costs while maintaining performance.
28. Ensure data engineering solutions adhere to regulatory and compliance requirements.
29. Collaborate with cross-functional teams, including data scientists and business analysts, to deliver data-driven insights.
30. Provide training and knowledge-sharing sessions to empower colleagues with data engineering best practices.