Rakshith Raghevendra Rao

Developed a group expense-splitting application that calculates per-person balances and proposes settlement suggestions. Implemented both the backend API and the user-facing interface for managing expenses and tracking settlements. Ran the solution in production and used a database layer to store group data and balances.

Built a web app that extracts and interprets mutual fund statement information from CAMS PDF files and enriches transactions using NAV data. Implemented the pipeline to parse PDFs, merge enriched data, and compute portfolio analytics. Exposed the results through a backend API and a dashboard for viewing analytics.

Owned the migration of a legacy Microsoft SQL Server warehouse to Databricks, re-platforming multiple tables and validating data against the source. Built and optimized PySpark transformation pipelines to improve runtime and reduce compute costs. Designed dbt dimensional models with tests and schema contracts, and maintained daily production jobs with Databricks Workflows to meet SLAs and resolve failures quickly.

Built incremental dbt models on BigQuery to transform raw datasets into analytics-ready outputs while reducing build time and scanned data. Developed a Python service to reconcile metadata between GCP Data Catalog (BigQuery) and Collibra, detect drift across assets, and raise JIRA tickets automatically. Implemented custom Collibra workflows and supported data governance by integrating Collibra with GCP via REST APIs and applying access controls using GCP IAM and policy tags.

Developed parts of retirement-related functionality in Python, including logic to recommend a retirement city based on user data. Delivered a digital payments feature to support direct disbursements to participants’ bank accounts. Worked on product features within the delivery team using Python-based implementation.