Data Management Analyst at The Sherwin Williams Company (2024-10 – Present)
Project Description: Enterprise Data Quality & Governance ModernizationWorked on enterprise-wide initiatives to improve data reliability, consistency, and governance across multiple business systems supporting operations, analytics, and reporting. The project focused on documenting data flows, validating enterprise datasets, and implementing standardized data quality controls aligned with corporate governance frameworks.
- Improved enterprise data reliability by designing and validating data quality controls across multiple business systems.
- Gathered and documented business requirements for a data governance tool, translating stakeholder needs into functional specifications to support platform enhancements and reporting standards.
- Served as a trusted advisor to business users on data governance tools and processes, providing guidance and training to improve adoption of data quality standards across teams.
- Strengthened organizational data governance by partnering with cross-functional teams to implement data quality frameworks and reporting standards.
- Enhanced decision-making capabilities by ensuring accurate data mapping, profiling, and validation across enterprise platforms.
- Reduced data integrity risks through proactive data testing, remediation tracking, and quality scorecard development.
- Configured and administered Collibra platform settings — including data cataloging, classification, and lineage tracking — to support enterprise data governance.
- Conducted comprehensive data profiling and validated data dictionaries to ensure accuracy, completeness, and consistency of organizational data assets.
- Performed source-to-target data mapping to support data integration, system migrations, and reporting initiatives.
- Maintained detailed documentation of data remediation activities and monitored issue resolution to improve data reliability and operational decision-making.
- Executed data testing procedures to verify consistency, accuracy, and compliance with organizational data quality standards.
- Developed and maintained data quality scorecards to measure performance metrics and proactively identify data risks.
Data Engineer at Finthrive (2023-08 – 2024-10)
Project Description: Large-Scale Cloud Data Engineering & Analytics Platform Designed and implemented large-scale data engineering solutions on cloud platforms to support advanced analytics, reporting, and AI-driven insights. The project involved building distributed data pipelines, optimizing data processing performance, and integrating governance and analytics tools.
- Built and managed metadata attestation and role-based access workflows in Collibra, defining role assignments and delegation processes for data owners and stewards.
- Promoted data governance best practices by supporting implementation of data quality frameworks, policies, and control processes across departments.
- Served as a trusted advisor to business and technical stakeholders on Collibra governance processes, providing guidance and training to improve platform adoption.
- Led data governance initiatives using Collibra to support research and reporting activities for healthcare/clinical studies, ensuring data compliance and integrity for clinical and administrative stakeholders.
- Wrote user stories and backlog items in JIRA to define requirements and track enhancements for data governance platform initiatives.
- Designed and implemented large-scale data pipelines and architecture — including Spark, Hive, Azure Databricks, Postgres, Amazon Aurora, and DynamoDB — to support governed, high-throughput data processing.
- Optimized database performance and integrated Power BI reporting across data platforms to support enterprise analytics.
Data Quality Engineer at CVS Health (2021-11 – 2023-07)
Project Description: Cloud Data Migration & Master Data GovernanceDeveloped data quality and governance initiatives during the migration of enterprise data from on-premises systems to cloud platforms. The project focused on validating migrated data, implementing master data management (MDM), and improving data accuracy across business systems.
- Built a robust system to validate the Quality if the data that has been migrated into the cloud environment from various on prem sources that the company has.
- Led the implementation of Informatica MDM, defining data models and establishing data governance policies that improved data accuracy by 20% and reduced data duplication across systems.
- Successfully migrated complex databases to new platforms, minimizing downtime and optimizing query performance.
- Experienced in collecting requirements from the Lines on Businesses and converting them into technical requirements for the developer teams to start working on them.
- Collaborated with cross-functional teams to assess client requirements and provide tailored GCP-based solutions.
Data Engineer at J.B. Hunt Transportation (2021-07 – 2021-10)
Project Description: Enterprise ETL & Cloud Data Warehouse Implementation Developed enterprise ETL pipelines and cloud data warehouse solutions to support large-scale analytics and reporting for a global e-commerce client.
- Developed ETL pipelines using Spark and Hive for performing various business specific transformations.
- Followed a defined Software Development Lifecycle (SDLC) to ensure quality and timely delivery of big data projects.
- Participated in all phases of the Project Development Lifecycle (PDLC), from gathering requirements to deployment and maintenance.
- Designed and implemented a scalable and performant Snowflake data platform for a global e-commerce company, handling petabytes of data and supporting real-time analytics.
- Collaborated with data scientists and analysts to develop advanced analytics solutions using Snowflake's integrated Snowpark and Snowflake Data Science capabilities.
