FinTech Data Pipeline at Scale
How 50+ serverless scrapers keep market and compliance data flowing for millions of users.
Problem & industry context
Regulated financial products depend on timely, accurate external data—rates, filings, partner feeds, and market signals. Manual collection does not scale; missed updates create compliance risk and stale customer experiences. Teams typically adopt event-driven, serverless pipelines to isolate failures, control cost per run, and ship new sources without redeploying monoliths.
Insight
Treat each data source as an independent contract: input schema, retry policy, dead-letter handling, and observability tags. Lambda fits bursty, parallel workloads when cold-start latency is acceptable and payloads stay within service limits. The real engineering win is operational—idempotent writes, structured logs, and alerts on freshness SLAs—not raw scrape count.
What I built
Designed and deployed 50+ AWS Lambda scrapers for a FinTech platform, automating collection and normalization across sources. Built resilient scheduling, error handling, and downstream handoff so product teams could trust fresh data without manual ops. Contributed to systems serving 2M+ users alongside broader AI and full-stack work at Digiflux.
Technical approach
Stack and tooling for this work: Python, AWS Lambda, Docker, MongoDB. Topics covered: AWS Lambda, Serverless, Data Engineering, FinTech.
Topics
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