• Rearchitected central components of event-sourced framework responsible for handling 15B Kafka messages daily, resulting in an 80% decrease in consumer lag and 95% CPU / memory usage reduction.

• Seamlessly migrated 500+ real-time consumers between frameworks w/o delays, errors, or data loss.

• Redesigned retry mechanism backing 800+ Kafka consumers using DynamoDB as an intermediate store, improving retry delivery time and compute efficiency, reducing CPU / memory usage by 93%.

• Introduced additional platform features: exponential backoff, DLQ, deeper monitoring + observability

• Developed a scalable pattern for building self-service tooling for devs to interact with the platform utilizing Lambdas.

• Discovered and resolved multi-year old bug in core libraries, bringing platform-sourced error rate closer to 0%.

• Added batch processing to custom data aggregation tooling, lowering write volume by 1000x during high load periods.

• Acquired by Sound.xyz

• Built self-healing data ingestion system to process, enrich, and index millions of blockchain transactions and async events per day using AWS SQS and Lambda.

• Designed and implemented paginated home feed architecture that scaled to support dozens of unique content types, as well as natively supported caching for expensive modules. This surface comprises 25% of all time spent in the app.

• Refactored bottomsheets across the app, preventing background rendering and app slowdowns of 15+ seconds for older mobile phones.

• Managed Postgres database quality and health, optimizing queries, indexes, and connection pooling via PGBouncer.

• Extended app for release on Android, reaching feature parity with iOS app.

• Developed recurring async process for tracking arbitrary NFT portfolios and calculating total value, including users with 10k+ items.

• Refactored core data structure to support multiple currencies and new user onboards without mass deletions and re-ingestion. Successfully completed mass migration of 50M+ rows.

• Implemented Datadog monitoring to track success rate of async tasks and notification sends which helped identify system outages and bring success rates close to 100%.

• Led 3 other engineers in effort to audit client-side logging before public app store release.

• Owned E2E implementation of personalized Shop and Product feed units which received 20M+ daily impressions and became one of the largest GMV drivers on Instagram.

• Became tech lead for resulting workstream - led 3 other engineers, developing specs for various UI optimization experiments for them to implement.

• Managed collaboration between 6 ML and product teams running experiments on our surface.

• Designed and implemented various experiments to maximize ads budget and promotion utilization, resulting in further GMV growth.

• Expanded feature to Stories surface, delegating client implementation to partner teams, adding 10M+ impressions daily to recommended Shops and Products.

• Extended Shopping Notification System by building data pipelines and backend to support notifying users of relevant new sales and discounts, increasing on-platform GMV.

• Presented tech talk on experimentation process and implementation to 20+ SWE and DS in IG Shopping pillar.

• Managed intern who built Shopping Notifications with topline impact to buyers. Intern later converted to full-time.

• Developed new Facebook Stories feature full-stack, from backend data model design to iOS client-side

• Extended new Objective-C framework for easier client-side development in the future

• Delegated tasks to full-time engineers to develop Android client-side for new feature

• Corrected service to better detect employee status changes and reduce unnecessary alerts

• Extended service to automatically transfer data pipelines as well as tables after employee status changes

• Created new asynchronous Python 3 service to notify employees of unsecured data pipelines