Somnath Sam

• Collaborated with cross-functional teams to define financial reporting requirements and operational performance metrics. • Extracted data from diverse internal sources using advanced Structured Query Language (SQL) joins and aggregations to support product analytics. • Developed and optimized datasets to improve dashboard responsiveness and data visualization accuracy within Power BI. • Created recurring reports for stakeholders to visualize weekly performance trends and deliver financial insights. • Implemented automated data quality checks for nulls and duplicates to enhance the reliability of reporting workflows. • Contributed to the development of self-service Business Intelligence (BI) tools that empower teams to make data-informed decisions.

• Collaborated with cross-functional teams to define financial reporting requirements and operational performance metrics.
• Extracted data from diverse internal sources using advanced Structured Query Language (SQL) joins and aggregations to support product analytics.
• Developed and optimized datasets to improve dashboard responsiveness and data visualization accuracy within Power BI.
• Created recurring reports for stakeholders to visualize weekly performance trends and deliver financial insights.
• Implemented automated data quality checks for nulls and duplicates to enhance the reliability of reporting workflows.
• Contributed to the development of self-service Business Intelligence (BI) tools that empower teams to make data-informed decisions.

• Validated large-scale transactional datasets weekly to ensure marketplace data integrity and alignment with quality standards. • Identified data mismatches within complex datasets to support data cleansing and transformation initiatives using Python. • Automated data reconciliation workflows using Structured Query Language (SQL) and Python within Jupyter Notebooks to improve operational efficiency. • Developed and updated Key Performance Indicator (KPI) dashboards to monitor weekly data accuracy and business goals. • Performed root-cause analysis on data discrepancy patterns to recommend targeted technical fixes and process improvements. • Supported the development of data collection processes by identifying invalid entries and streamlining automated reporting solutions.

• Validated large-scale transactional datasets weekly to ensure marketplace data integrity and alignment with quality standards.
• Identified data mismatches within complex datasets to support data cleansing and transformation initiatives using Python.
• Automated data reconciliation workflows using Structured Query Language (SQL) and Python within Jupyter Notebooks to improve operational efficiency.
• Developed and updated Key Performance Indicator (KPI) dashboards to monitor weekly data accuracy and business goals.
• Performed root-cause analysis on data discrepancy patterns to recommend targeted technical fixes and process improvements.
• Supported the development of data collection processes by identifying invalid entries and streamlining automated reporting solutions.

• Supported the validation of end-to-end data flows across multiple enterprise platforms during software release cycles. • Verified data accuracy and completeness between source and target datasets to ensure consistency across the analytics pipeline. • Executed Structured Query Language (SQL) scripts to perform row count checks and identify duplicate records for data cleansing. • Documented data defects in Jira to track resolution status and minimize recurring issues in database maintenance. • Created standardized data validation checklists and sign-off criteria to maintain data quality across various environments. • Improved documentation procedures for the global analytics team to ensure clarity and accuracy in reporting workflows.

• Supported the validation of end-to-end data flows across multiple enterprise platforms during software release cycles.
• Verified data accuracy and completeness between source and target datasets to ensure consistency across the analytics pipeline.
• Executed Structured Query Language (SQL) scripts to perform row count checks and identify duplicate records for data cleansing.
• Documented data defects in Jira to track resolution status and minimize recurring issues in database maintenance.
• Created standardized data validation checklists and sign-off criteria to maintain data quality across various environments.
• Improved documentation procedures for the global analytics team to ensure clarity and accuracy in reporting workflows.