Leads scaling and latency reduction improvements of Zendesk’s core APIs that receive up to a billion requests per day to improve customer experience and reduction in cloud cost.

• Identifies system bottle-necks and develops solutions using DataDog’s Application Performance Monitoring (APM) software, improving API response times and customer experience and reducing AWS costs

• Successfully reduced core APIs p99 latency by 40% and averages by 30% through extensive code refactoring, query optimization and caching techniques

• Designs and implements stream processing system to process asynchronous ticket updates using Kafka Streams

• Improves query execution times by restructuring and introducing new indexes in AuroraDB. Guarded primary DB and achieved faster responses by moving data fetching to ElasticSearch

• Re-architected data retrieval layer by transitioning from MySQL to Elasticsearch, optimizing indexing and query logic to support real-time analytics with lower latency and improved fault tolerance

• Ensures code releases are extensively tested and guarded by feature flags to guarantee no service disruption and reduction in performance. Quantifies key measures after changes through performance metrics analysis

Improving scaling and latency reduction of Zendesk’s core APIs that receive up to a billion requests per day to improve customer experience and reduction in cloud cost.

• Identifies system bottle-necks and develops solutions using DataDog’s Application Performance Monitoring (APM) software, improving API response times and customer experience and reducing AWS costs

• Successfully reduced core APIs p99 latency by 40% and averages by 30% through extensive code refactoring, query optimization and caching techniques

• Improved query execution times by restructuring and introducing new indexes in AuroraDB. Guarded primary DB and achieved faster responses by moving data fetching to ElasticSearch

• Created various DataDog dashboards for performance monitoring, system’s SLAs and error budget notification

• Ensure code releases are extensively tested and guarded by feature flags to guarantee no service disruption and reduction in performance. Quantifies key measures after changes through performance metrics analysis

Developed system to analyze, monitor, and predict population-level knowledge. This system is implemented in Python, managed using Kubernetes and Docker and hosted in AWS.

• Implemented data pipeline using Python and SQS to ingest, transform and store information in S3 and MongoDB. Integrated machine learning models in data pipeline to generate predictions

• Designed scalable and low latency APIs using efficient algorithms and architectural patterns that provide low latency and high throughput using Python, MongoDB and Memcached as data storage

• Created single-page web-app using React, Redux, MapBox and D3.js for information visualization and charting

• Working on managing Docker containers and Kubernetes container orchestration

Worked on extending and improving existing fulfillment system. Successfully lead, implemented and released platform to launch and manage new product lines of customizable inventory.

• Worked on extending and improving fulfillment system by implementing data pipelines and jobs using Python, AWS SQS and MySQL that were horizontally scalable during peak load

• Lead team of 5 engineers to architect, implemented and release platform for launching new product lines using Python, Flask and MySQL

• Maintained and improved RESTful APIs for third party integration for order fulfillment

• Collaborate with product managers to deliver projects with measurable business results

Working on creating cloud platform from ground up for managing the workflow of incidental findings in radiology reports identified by Natural Language Processing and Machine learning algorithms.

• One of the first developers involved in designing and implementing SaaS by implementing RESTful APIs and background processes using .Net Core, C# in Azure setting.

• Implemented messaging queues using Apache Kafka for high throughput, reliability and fault tolerance.

• Used Test Driven Development (TDD) to ensure highest level of software quality.

• Working closely with product owner, business analysts and team members using agile methodology.