Utkarsh D.

- Founded a legaltech startup to help Indian lawyers automate their time-keeping and billing, saving them time and increasing their revenue. Led product development, sales, marketing, and development. - Built an Azure app to integrate with law firms' timesheets in OneDrive, and automatically generate invoices from them; used Oauth 2, Microsoft Graph API, and Pandas. - Used Oauth 2 and Microsoft Graph API to build an Azure app that automatically generates summaries of billable work lawyers have done by reading from their Outlook emails and calendars. - Extended this functionality to Google accounts through the use of Oauth 2, Gmail API, and Google Calendar API. - Created AWS Lambda functions with API Gateway endpoints that end-users could access to generate invoices and summaries of their billable work whenever they wanted. - Used AWS SES to deliver invoices and billable work summaries to lawyers whenever they were generated. - Called/emailed potential clients, demoed our product to several potential users, and successfully set up customers with the product for usage. - Designed website (www.firmation.in) and handled marketing using Google Ads. - Wrote a Python script to automatically contact potential leads on LinkedIn.

- Designed and developed a production data warehouse with denormalized tables in BigQuery using a MongoDB database on Heroku as the data source and Stitch Data as an ETL tool. - Scheduled extractions from MongoDB every six hours using Stitch Data, ensuring that only recently-updated data was included. - Created scheduled query to join and load data to a denormalized table in BigQuery after extraction from MongoDB is complete. - Created graphs and geospatial plots from BigQuery data for customer demo to his client using Plotly and Jupyter Notebooks. - Thoroughly documented instructions for setting up and querying BigQuery for future developers working on the project. - Researched integrating Google Analytics into BigQuery to track the customer lifecycle from acquisition onwards. - Created views on the denormalized BigQuery table to allow users to easily see the most recent state of the database. - Worked closely with QA lead to the end-to-end test data warehouse, from automated extractions to loads and views.