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.

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.

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.

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

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