• Building the next-generation Agentic experience through optimizing LLMs and enhancing next-generation Music models. Built data pipelines for models to streamline low-latency and fresh computational data.

• Updated batch pipelines for Spotify Wrapped 2025 to include yearly listening history and statistics, for a 1 billion+ user release.

• Created Lookr Studio dashboards and BigQuery visualizations for identifying view states for optimization and deprecation, reducing yearly costs by $1M+. Developed internal Web tool for ML engineers to visualize their data-streaming pipelines and events, showing dependencies, metrics, and configurations.

• Backend and Fullstack Engineer for Market Intelligence, Securities, and Ledger team, developing real-time market data systems for Invest product and financial systems.

• Developed Full-stack internal tool application to enable traders to manually update stock information, corporate actions, and update transactions of Sofi users.

• Collaborated with cross-functional teams to ensure seamless system integration, scaling, and support

• Architected real-time market data systems for the Invest product, processing 200,000+ events/minute

using Kotlin, Java, AWS and Terraform, enhancing data reliability and scalability.

• Streamlined on-call operations by creating Datadog monitors and runbooks, reducing incident response time

and ensuring production stability for high-traffic financial systems.

• Created large-scale data platform pipelines using GCP, Apache, and Scio. Created data processing frameworks using Apache Spark

• Integrated data providers to ingest real-time market data analysis and information via web-sockets and

workflows. (PostgresSQL, Redis, Kotlin/Java, OpenAPI, Kafka, Temporal, Kubernetes)

• Friend Interactions backend engineer developing new high-priority consumption and creation experiences

• Researched and developed Story Avatar Circle to introduce Stories on Tiktok and increase consumption

by over 100%.

• Implemented new Skylight feature to show users’ stories in feeds and unblock filtered collection content.

• Optimized services improving latency, metrics, and outage detection infrastructure. Drastically increased Story consumption and creation services by +10% launching feature experiments in Go, Python, Redis, AWS, and Grafana.

• Developed an automated load-testing tool in Python to synthetically stress test load on several services to

detect patterns to prevent 1,000+ large scale outages on AWS clusters.

• Added MongoDB support on queries for aggregation and randomization tests to replicate user load.

Researched breaking points of clusters to optimize bottlenecks of jobs and increase capacity limits of services