Mark Kim

As a back-end developer, I worked with various stakeholders to deliver functionality ranging from enhanced reporting capabilities for internal teams, third-party API integrations for real-time data analysis, GraphQL queries, and mutations for customer-facing front-end features to manage deposited funds and compliance data, and new endpoints and features for RESTful APIs integrated by our clients. The back-end stack consisted of microservices developed in Go, leveraging GRPC, Docker, Kubernetes, MySQL, gqlgen, and Swagger. Additionally, tooling for RESTful APIs was maintained in Python and used for demo and testing purposes, in addition to load testing.

I was the back-end engineer tasked with the integration of the third-party ad inventory for a new product on a major top-10 mobile advertising network. The third-party APIs were integrated to conform to a single common interface within an ad server built in Go using go-kit and custom queueing infrastructure for parallel processing of ad data ingestion. The role also required interfacing with the third-party's support teams for integration-related debugging and ongoing operational issues. The stack also consisted of MySQL, Couchbase, Kubernetes, and Ruby/Rails for additional tooling.

During Zynga's transition from on-prem data centers to the cloud, I was in charge of the migration of ad serving infrastructure and the core ad server, which represented over 1B API calls a day combined across all components. The deployment tools for the various systems were based on Ansible that is implemented in Python. The scope of the work was to update our Ansible tools to deploy to our new cloud platform and implement and update custom modules in Python that were added for our specific cloud deployment needs. This included Python modules to implement server health checks, monitoring, deploy notifications, as well as rolling deploys of new server code across a fleet of 200+ nodes.

I migrated a high-performance RESTful API built on Java from an on-premise data center to AWS for a Fortune 1000 company. The API serviced over one billion calls daily and supported all ad revenue at the company, which accounted for over one-third of the company's overall revenue. As the sole engineer on the project, I planned out the required resource and service footprint within AWS, implemented necessary changes to support a smooth migration, and executed a phased plan for migrating the service with zero downtime. The project was completed within the aggressive timeline set as part of a larger effort to move all back-end services to AWS. The project also led to a more efficient footprint through a reduced server count, improved success rate metrics, and increased stability.

A Python/Go-based eCommerce platform processing and supporting the shipment of millions of packages a month and integrating with all major eCommerce platforms like Shopify, Amazon, Wix, and more.

Project details:
• Acted as the back-end engineer focused on third-party API integrations importing the order information from eCommerce channels like Shopify, Wix, eBay, WooCommerce, and Square involving REST/GraphQL on the Django/Go stack
• Implemented a scalable queuing solution to process incoming Shopify customer orders with retries, exponential backoffs, and row-level table locking with transactions able to process 100,000+ orders per day
• Drove system-level debugging on the live production environment involving performance issues and faulty API integrations

I worked as a senior back-end engineer focused on third-party API integrations into eCommerce platforms like Shopify, Wix, eBay, WooCommerce, and Square, involving REST/GraphQL on Django-Python and Go stack. I implemented a scalable queuing solution to process incoming Shopify customer orders with retries, exponential backoffs, and row-level table locking with transactions able to process 100,000+ orders/day. I led the migration of a major Amazon Selling Partner API store integration, transitioning from a legacy API to Amazon's new OAuth 2.0-based API. I also performed drive system-level debugging on a live production environment involving performance issues and faulty API integrations.