Somnath Samanto

- Collaborates with cross-functional teams to define Governance and Experience (GNE) operation metrics and technical reporting requirements. - Builds curated datasets using Structured Query Language (SQL) joins and aggregations to support diverse product analytics and experimentation use cases. - Improves dashboard responsiveness by optimizing query logic and implementing data preparation steps for large-scale datasets. - Develops interactive Tableau dashboards for stakeholders to visualize weekly performance trends and operational GNE insights. - Implements automated data quality checks for nulls, duplicates, and validity to enhance overall marketplace reporting reliability. - Contributes to the reduction of recurring data anomalies through the application of rule-based validation techniques and data profiling.

• Collaborates with cross-functional teams to define Governance and Experience (GNE) operation metrics and technical reporting requirements.
• Builds curated datasets using Structured Query Language (SQL) joins and aggregations to support diverse product analytics and experimentation use cases.
• Improves dashboard responsiveness by optimizing query logic and implementing data preparation steps for large-scale datasets.
• Develops interactive Tableau dashboards for stakeholders to visualize weekly performance trends and operational GNE insights.
• Implements automated data quality checks for nulls, duplicates, and validity to enhance overall marketplace reporting reliability.
• Contributes to the reduction of recurring data anomalies through the application of rule-based validation techniques and data profiling.

- Validated large-scale geospatial datasets comprising 200,000 Points of Interest (POIs) weekly to ensure marketplace data integrity. - Identified invalid coordinates, duplicates, and category mismatches within complex datasets to maintain high trust and safety standards. - Automated data reconciliation and validation workflows using Structured Query Language (SQL) and Python pandas library within Jupyter Notebooks. - Developed and maintained Key Performance Indicator (KPI) dashboards to monitor weekly data integrity and operational goals for 10 stakeholders. - Performed root-cause analysis on data discrepancy patterns to recommend targeted process improvements and technical fixes for business partners. - Assisted in reducing manual review effort and recurring errors through technical optimization and automated reporting solutions.

• Validated large-scale geospatial datasets comprising 200,000 Points of Interest (POIs) weekly to ensure marketplace data integrity.
• Identified invalid coordinates, duplicates, and category mismatches within complex datasets to maintain high trust and safety standards.
• Automated data reconciliation and validation workflows using Structured Query Language (SQL) and Python pandas library within Jupyter Notebooks.
• Developed and maintained Key Performance Indicator (KPI) dashboards to monitor weekly data integrity and operational goals for 10 stakeholders.
• Performed root-cause analysis on data discrepancy patterns to recommend targeted process improvements and technical fixes for business partners.
• Assisted in reducing manual review effort and recurring errors through technical optimization and automated reporting solutions.

- Supported over 30 software releases by validating end-to-end data flows across multiple enterprise e-commerce platforms. - Verified data accuracy, completeness, and reconciliation between source and target datasets during complex software release cycles. - Executed Structured Query Language (SQL) validation scripts to perform row count checks, identify duplicates, and ensure referential integrity. - Documented data defects in Jira to track resolution status and minimize recurring production issues in the analytics pipeline. - Created standardized data validation checklists and sign-off criteria to maintain consistency across various testing environments. - Improved release validation cycles by streamlining testing and documentation procedures for the global analytics team.

• Supported over 30 software releases by validating end-to-end data flows across multiple enterprise e-commerce platforms.
• Verified data accuracy, completeness, and reconciliation between source and target datasets during complex software release cycles.
• Executed Structured Query Language (SQL) validation scripts to perform row count checks, identify duplicates, and ensure referential integrity.
• Documented data defects in Jira to track resolution status and minimize recurring production issues in the analytics pipeline.
• Created standardized data validation checklists and sign-off criteria to maintain consistency across various testing environments.
• Improved release validation cycles by streamlining testing and documentation procedures for the global analytics team.