Mohit Srivastava

• Engineered and optimized end-to-end data pipelines using Databricks and PySpark to transform raw data for advanced analytics.
• Collaborated with fellow engineers on migrating workflows to Simpel 2.0, ensuring MARS standards compliance and enhancing data processing.
• Automated ETL processes, data ingestion, and DAG creation using Python, SQL, and Azure DevOps, resulting in a 30% boost in efficiency.
• Integrated CI/CD pipelines, managing PySpark scripts and whl files on Databricks clusters, speeding up release cycles.
• Conducted post-release optimizations and backend reporting, reducing data discrepancies by 15% and improving data stability.
• Led data quality initiatives through rigorous unit testing and validation, ensuring robust, error-free deployments.
• Managed tasks from code lineage to pipeline monitoring and backlog enhancements

Project: McAfee Data Governance

• Improved data quality by reducing junk data to below 5% through enhanced telemetry validations and comprehensive L3/L4 checks.
• Implemented data validation rules within Great Expectations framework to enhance data integrity.
• Configured new attributes and events for every release, optimizing data tracking and reducing errors.
• Collaborated on a POC with the Data Engineering team, implementing data lineage solutions and refining data governance practices.
• Boosted team's proficiency through targeted training

Project: Adobe Experience Manager (AEM)

• Analyzed and tested CMS components, enhancing system functionality and security.
• Increased code coverage to over 60% by writing JUnit tests, enabling automated CI/CD pipelines.
• Addressed security vulnerabilities and infrastructure issues, improving the post-deployment performance of global websites.