BHARAT SIKARWAR

• Designed and developed scalable ETL/ELT pipelines using PySpark, SQL, and AWS services to process over 5TB+ of structured and semi-structured data daily from multiple source systems.
• Built and optimized cloud-based data warehouse solutions in Snowflake, improving query performance by 40% and reducing data processing costs by 25% through efficient warehouse sizing and clustering strategies.
• Leveraged Amazon Web Services services including S3, Glue, Lambda, Athena, IAM, and CloudWatch to automate data ingestion and monitoring workflows, reducing manual intervention by 60%.
• Developed high-performance PySpark transformations and advanced SQL queries that reduced ETL job execution time from 4 hours to less than 1.5 hours, improving overall pipeline efficiency by 65%.
• Designed dimensional data models, implemented automated data quality validation frameworks, and reduced data inconsistencies by 35%, ensuring reliable reporting and analytics for business users.
• Led migration of legacy on-premise data platforms to AWS and Snowflake, successfully migrating 100+ critical datasets and improving system scalability and availability by 50%.
• Collaborated with business stakeholders, architects, and cross-functional teams in Agile environments to deliver enterprise-scale data solutions, achieving 95% on-time project delivery and improved stakeholder satisfaction.

• Supported large-scale data migration initiatives from legacy/on-premise systems to cloud-based data platforms using modern ETL/ELT frameworks, ensuring high data accuracy and seamless transition across environments.
• Performed end-to-end data validation, reconciliation, and source-to-target verification for enterprise data pipelines, reducing data discrepancies by 40% and improving overall data reliability.
• Developed complex SQL scripts and automated validation queries for data profiling, root cause analysis, and troubleshooting, improving defect detection and resolution efficiency by 30%.
• Automated repetitive ETL validation and regression workflows using Python and SQL, improving operational efficiency by 25% and reducing manual effort.
• Built and maintained scalable ETL pipelines using Python, SQL, and Spark for batch data processing and ingestion, reducing overall processing time by 35%.
• Optimized data models, transformation logic, and query performance to support scalable analytics and improve reporting efficiency by 20%.
• Implemented data quality frameworks, validation checks, monitoring dashboards, and exception handling mechanisms to ensure trusted and consistent data delivery across business domains.
• Collaborated with analytics, business intelligence, and cross-functional teams to deliver clean, validated, and production-ready datasets for reporting, dashboards, and machine learning use cases.

• Performed large-scale data validation, integrity checks, and reconciliation across multiple database environments to ensure high data accuracy, consistency, and reliability for enterprise data platforms.
• Developed and optimized complex SQL queries, stored procedures, views, and transformation logic for efficient data extraction, validation, and reporting processes.
• Implemented database optimization techniques including query tuning, indexing strategies, and execution plan analysis, improving query performance and reducing processing bottlenecks.
• Automated SQL validation, reconciliation, and database monitoring processes using scripting and scheduling frameworks, reducing manual effort and improving operational efficiency.
• Supported ETL/ELT workflows and data migration activities during system modernization and cloud transformation initiatives, ensuring zero data loss and seamless data transition.
• Identified and resolved data quality, performance, and query optimization issues, reducing production incidents by 30% and improving system stability.
• Collaborated with data engineers, developers, DBAs, and cross-functional teams to enhance database performance, optimize data workflows, and support scalable analytics solutions.
• Ensured adherence to data governance, security, compliance, and backup/recovery standards while managing enterprise-scale database operations and data processing activities.

Executed data integrity, consistency, and accuracy checks across multiple environments for reliable database operations.

• Wrote and executed complex SQL queries to validate CRUD operations and ensure data correctness.
• Tested stored procedures, triggers, views, and indexing strategies for optimal functionality and performance.
• Conducted functional, regression, and performance testing on database systems and applications.
• Validated data migration and backup processes during system upgrades, achieving zero data loss.
• Identified defects related to data integrity and query optimization, reducing production issues by 30%.
• Collaborated with developers and DBAs to address database-related defects and enhance query performance.
• Ensured compliance with data security, governance, and regulatory standards throughout testing activities.

• Analyzed business and technical requirements to support database operations, data validation, and production support activities across multiple environments.
• Developed and executed complex SQL queries for data verification, troubleshooting, and ensuring database accuracy and consistency.
• Provided database support for functional, integration, and regression testing activities to ensure stable and reliable application performance.
• Performed database monitoring, issue analysis, and defect tracking, enabling faster root cause identification and resolution of production issues.
• Supported manual and automated database validation processes to improve operational efficiency and reduce recurring issues.
• Collaborated with developers, QA teams, and DBAs to resolve database-related defects and enhance overall system reliability and performance.