CASE_01 · 50x parallel · ~24h · 100M files
100M files in 24 hours
An IP litigation firm shipped us a hard drive: 100 million recursively-compressed discovery documents they needed searchable.
Instead of their weeks-long Windows pipeline, I pushed everything to S3 and built a Fargate cluster running depth-first decompression with SQS as the stack. Fifty parallel tasks chewed through all 100M files in about a day, indexed via S3 inventory for retrieval.
AWS Fargate · SQS · S3 · Python
CASE_02 · 90%+ faster · 1000x fewer bytes
The 1000x dashboard
Motional's BI dashboard downloaded entire datasets to the browser — and crashed on the ones that mattered.
I identified the architectural dead end, proposed the migration, and led the API schema design: DynamoDB out, Redshift's columnar engine in, Fargate services in between. Six engineers, six months, 90%+ load-time improvement on the tooling AV engineers used daily.
Redshift · DynamoDB · Fargate · React
CASE_03 · 2B+ req/day · 10+ teams · 0 incidents
Defusing XSS at 2B req/day
A long-known XSS vector in Google's ad iframes — on a surface serving billions of requests a day, where any mistake is a headline.
Led the fix end-to-end: coordinated 10+ partner teams, sat with lawyers and external CEOs, and ramped experiments from 0.1% to 100% of traffic. When the first ramp showed -100% revenue from bad HTTPS certs, killed it in minutes, fixed root cause, re-ran clean.
TypeScript · Closure · A/B infrastructure
CASE_04 · 67k users · 1.8M searches · alive 10 years
Search NEU
As a student: search every class and professor at Northeastern, faster than the registrar could.
Scraped 1M+ pages a day across 10+ sites, indexed into Elasticsearch with sub-20ms responses, ran at 99%+ uptime on AWS. Handed it off to four student founders — it's still running a decade later.
Elasticsearch · AWS · React · Node