Hi, I am Surbhi Bhor

Designed and enhanced QuickSight dashboards across Supplier Marketing, Parcel Returns, and Seasonal Reporting programs, enabling leadership to track KPIs such as sales lift, new-to-brand lift, YoY growth, return types, and same-day conversions.

Developed automated ETL pipelines using Spark SQL (Cradle) and Redshift to extract from Andes/S3, transform large datasets, and load into reporting data marts — reducing data processing time by 35% and minimizing manual errors.

Optimized Spark SQL queries by 25% through partitioning, broadcast joins, and resource tuning, improving pipeline stability and efficiency.

Automated ingestion of manual source trackers with AWS Lambda and S3, ensuring timely seasonal reporting and eliminating delays caused by manual processes.

Partnered with business stakeholders to define metrics and create data flow diagrams; validated Redshift SQL queries and dashboard logic, ensuring accuracy, scalability, and delivering visibility into ~$5.9M in supplier program entitlements.

Built Azure Synapse data pipelines to initiate ETL process for efficient initial and incremental data loads across data layers

Transformed and curated over 25000 records into a staging Hive table using ADLS and data warehouse resulting in a SCD1 delta table, reducing data discrepancies by 75%

Implemented data quality validation with PySpark notebook, resulting in the identification and resolution of a critical bug, enhancing data integrity

Strategically assessed six ERD migration tools, seamlessly transitioning to the optimal solution, resulting in enhanced real-time collaboration and significant time-saving advantages

Orchestrated comprehensive data modelling for a complex domain spanning 15 diverse tables, translating it into an effective ER diagram and further enhancing understanding through strategic creation of data flow diagrams for multiple pods

Led the development and implementation of automated testing procedures through Jenkins, ensuring the seamless deployment of software releases for the backend of the application.

Directed and maintained a robust suite of automation test scripts utilizing an internal AWS framework comprising lambda, step function, and API gateway. Leveraged lambda functions and state machines to intricately chain together function calls, optimizing test case outcomes.

Integrated smoke, regression, and integration test suites into the Azure DevOps CI/CD pipeline using API gateway, enhancing the efficiency of the continuous integration and deployment processes.

Elevated test case coverage by 70% through the automation of critical AWS functionalities, including Cloudwatch logs and DynamoDB.

Developed and scripted over 200 API test cases for smoke, regression, and integration scenarios, contributing to the overall robustness of the testing framework.

Collaborated closely with the manual test team to analyze detailed test requirements, subsequently creating comprehensive test data sheets for automation.

Pioneered the creation of reusable actions within automated test scripts, facilitating streamlined adaptation to automation code changes across various microservices in response to evolving functionalities.