Mridul Raj Email & Phone Number

• Conducted requirement analysis and system design for the Integration and Analytics.• Designed and implemented ETL Pipeline using Azure Data Factory to ingest and transform.• Utilized Azure Databricks for data cleaning, transformation, and advanced analytics, ensuring data quality and integrity.• Successfully integrated diverse data sources, leading to a 30% improvement in data processing efficiency• Enabled real-time monitoring and alerting of network security incidents, significantly enhancing proactive threat management.

Greater Kolkata Area

• Conducted performance tuning on spark cluster, optimized to save data ingestion time by 20%.• Developed a PowerShell script to automate spark client jobs that were previously executed manually, resulting in a 100%reduction in human effort.• Created data validation VBA, resulting in an 80% time-saving in data validation.• Produced detailed documentation outlining code modifications and encountered errors along with their causes and resolutions. Facilitated effective knowledge transfer to the internal team, ensuring smooth ongoing support and maintenance• Analyzing the logs to find the cause of the error and finding the resolution in very little time to avoid data accumulation for incremental load• Designed and implemented an SQL query leveraging an audit table to perform incremental data checks in a database environment. The query effectively identifies changes based on timestamp differentials, facilitating incremental data extraction.• Automated the execution of this query at 30-minute intervals, ensuring timely and efficient data updates• Conducted detailed planning and risk assessments, ensuring thorough preparation and mitigation strategies.

Greater Kolkata Area

• Led a Project within the Finance Domain utilizing PySpark and Azure Cloud, streamlining lending processes for emerging banks and delivering a notable 20% decrease in processing time.• Streamlined data from multiple sources, performed transformations and aggregations and loaded the refined data into the target system.• Employed advanced data cleansing methodologies to address 95% of duplicate entries, null values, and data type inconsistencies, guaranteeing impeccable data integrity for subsequent operational teams.• Deployed effective Slowly Changing Dimensions (SCD) techniques, enabling smooth handling of updates and contributing to a significant 25% enhancement in data accuracy.• Tools In Use: My SQL, Pyspark , Linux, Powershell , VBA , Macros.