Kanika Gupta

Client: Green Street

• Developed end to end automated 30+ data ingestion pipelines extracting data from third

party sources serving USA, Europe, Canada regions for Green Street Advisors - a commercial

real estate analysis, research, and advisory firm

• Developed end to end automated data pipelines to process large datasets from various sources.

• Developed backend APIs using Fast API to serve ingested data with role-based access con

trols.Responsible for database schema design, data ingestion, validation, quality checks, no

tification systems

• Implemented data validation and quality checks using Great Expectations(GX) in the data

pipeline to ensure accuracy and consistency of data.

• Tools/Frameworks: Python, AWS Lambda, AWS S3, AWS ECS, Airflow, FastAPI, MySQL, Pan

das, Polars, GX

Client: RiskRating

• Developed Flask APIs for a client, implementing CRUD operations specifically for the admin role.
• Utilized Flask, Python, SQLAlchemy, and Alembic for API development and database management.
• Employed PostgreSQL as the database solution for robust data storage and management.
• Managed version control and project collaboration using GitLab.

ADR

• Refactored 57 ADR APIs, updated their documentation, and rigorously tested them for both associate and reporting manager roles.
• Developed CRUD APIs for the ADR cycle, enabling seamless cycle creation from the front end.
• Conducted integration testing of the ADR application with the HRMS system to ensure smooth interoperability.
• Deployed the ADR application on the server and managed it using screen, a terminal multiplexer tool for persistent sessions.

Client: Ene-Onsite:

• Upgraded the existing system from Openerp to Odoo framework, ensuring a smooth transition and improved functionality for the organization
• Conducted thorough development testing to validate implemented features and functionalities, identifying and resolving bugs and issues to maintain system performance
• Validated the result of API points by comparing them with PostgreSQL query results, ensuring data accuracy and consistency

Client: AtlPar-Data Reconciliation Solution:

• Reviewed and made necessary modifications manually to the extracted data, particularly filling in any missing information in the converted Excel files
• Developed Python scripts to perform automated checks on the total values obtained from the PDF and Excel files using Pandas and Excel libraries, ensuring data accuracy and consistency