sandhya venkata

Leading financial reporting and transaction analytics for TD's banking analytics team — working across Power BI, T-SQL, Azure Data Factory, and Microsoft Fabric. — Designed and maintained the Loan Performance and Branch Sales dashboards serving 50+ stakeholders daily — built on Star Schema models with DAX measures and Row Level Security — Leading Power BI to Microsoft Fabric Lakehouse migration with zero quality defects at cutover — managed phased validation checkpoints and a daily control workbook tracking dataset status across the full migration — Identified and resolved a source system defect silently corrupting multiple leadership dashboards — traced root cause, corrected 12 months of historical records, and documented findings in Confluence — Reconciled banking transaction data daily between Azure Data Factory pipelines and SQL Server — catching count and value discrepancies before reports reached senior leadership or compliance teams — Built Confluence documentation standard covering DAX logic, ETL rules, and data lineage — adopted by the full analytics team, reducing onboarding time and knowledge gaps — Prevented incorrect data from reaching a board-level presentation under senior deadline pressure — stakeholder confirmed it as the right call

Owned data validation, reporting, and pipeline quality across Canadian Tire's retail analytics team — working across Power BI, Power Query, T-SQL, and ETL pipelines serving 4 to 6 store systems daily. — Built an automated three-stage Power Query validation pipeline that eliminated 20 hours of manual pre-release effort per week — adopted as team standard and remains in active use today — Detected a critical ETL defect causing sales transactions to be systematically double-counted across an entire store region — isolated root cause and corrected records before any stakeholder was impacted — Resolved a six-week undetected error in district-level sales figures — traced root cause, corrected historical records, and introduced a permanent weekly variance control that prevented recurrence — Built and maintained 12 Power BI reports covering sales performance, inventory KPIs, and regional metrics — using DAX, Power Query, and scheduled refresh for daily accuracy — Validated daily sales and inventory feeds from 4 to 6 retail store systems — applying completeness, range, and cross-table checks before any KPI report reached a stakeholder — Collaborated with engineering teams to align ETL pipeline design with downstream reporting requirements — improving reliability across 5 reporting domains

Analyzed transactional and customer datasets to identify performance patterns and operational insights for business stakeholders. Key contributions: • Designed complex SQL queries (joins, CTEs, aggregations) to extract and transform data from relational databases for KPI reporting • Built Power BI and Tableau dashboards monitoring sales performance, customer acquisition, and operational metrics • Conducted EDA using Python (Pandas, NumPy) to identify correlations and anomalies in large datasets • Automated recurring reports using Python scripts, improving reporting efficiency and reducing manual effort • Performed data cleansing, validation, and transformation to ensure accuracy across reporting systems

Generated daily, weekly, and monthly operational reports using SQL and advanced Excel to support business performance monitoring. Key contributions: • Developed Excel dashboards using Pivot Tables, Power Query, and advanced formulas to improve reporting efficiency • Assisted senior analysts in building Power BI dashboards to visualize KPIs and business trends • Automated recurring reports using Excel macros and SQL scripts, reducing manual reporting effort • Performed data cleansing and validation to ensure accuracy and consistency across reporting systems

MBA with specialization in Information Systems — covering business strategy, data-driven decision making, systems analysis, and enterprise management. Foundation for bridging business requirements with technology solutions — directly applicable to Business Analyst and Data Analyst roles.