Sanjeevan Ambalavanar

Suggested is a customer feedback tool for product managers. A highlight of this project was building the MVP of Suggested within six months and launching it on Product Hunt, where it was voted the #1 Product of the Day.

I was involved in designing the product from scratch. This involved talking to customers about their pain points, collecting data about how customers interacted with the app, and making product decisions. Because Suggested is an app to collect feature requests from customers, we were able to "dogfood" our software with our customers and iterate quickly to build the product.

I owned back-end and front-end development, which included the following:
• A complete SPA front-end built in React, using Redux for state management.
• A Flask and Python back end powering API endpoints.
• Redis for caching.
• Elasticsearch for power search and duplication check.
• Docker Swarm to run back-end applications.
• Integrations with Slack and Intercom.
• Integrations for Slack and Intercom using their APIs.

I was initially brought on as a contractor to build the MVP, which evolved into a permanent position as Line-Up's CTO. I led the development of the core back-end infrastructure that enables Line-Up to sell tickets at scale. The stack consisted of a Flask and Python API back end hosted on an autoscaling cluster on AWS with data stored on a MySQL RDS instance. We used Elasticsearch extensively to power event searches and filtering by geolocation.

I was also responsible for building most of the user-facing products. This included a React-based embeddable app used by customers to order tickets and an admin interface for venues to manage inventory and venue information.

Morta is a platform for the construction industry to manage and share data related to construction projects. Think of it as Airtable for the construction industry.

I was involved in designing and implementing key product features across the back end and front end. These included the React spreadsheet component on the front end and the back-end async task system that allows users to define custom Python scripts that compute values for spreadsheet columns. I also implemented best practices for the engineering team, such as staging and testing environments, unit and integration testing, code reviews, and automated CI.