Real-Time Data Pipeline on AWS + Snowflake for FinTech

A Series C FinTech lending platform processing $200M in monthly loan originations relied on a legacy batch-processing data system that updated dashboards once every 12 hours. Risk analysts were making credit decisions on stale data, leading to a 3.2% increase in default rates compared to competitors using real-time signals. The existing system used a single PostgreSQL instance as a data warehouse — query performance had degraded to 45+ seconds for complex risk reports as data volume grew to 800GB. The company needed real-time fraud detection (sub-100ms), streaming risk scoring, and a modern data platform that could support their 3x growth projections over the next 18 months. Their in-house team of 3 data engineers was fully occupied maintaining the existing system.

We provided a 5-person offshore team: 2 senior data engineers, 1 Snowflake architect, 1 AWS infrastructure engineer, and 1 quality/testing engineer. Phase 1 (weeks 1-4): Architecture design — Amazon Kinesis for real-time ingestion, AWS Lambda for stream processing, Snowflake for the warehouse, and dbt for transformation layer. Designed a medallion architecture (bronze/silver/gold) for data quality management. Phase 2 (weeks 5-10): Built the streaming pipeline ingesting events from 14 source systems — payment processors, KYC providers, credit bureaus, and application databases. Implemented exactly-once processing semantics and dead-letter queue handling. Phase 3 (weeks 11-14): Snowflake warehouse implementation — Snowpipe for micro-batch loading (5-minute latency to warehouse), 47 dbt models for the transformation layer, and materialized views for the most-accessed risk dashboards. Phase 4 (weeks 15-18): Real-time fraud detection engine using AWS Lambda + DynamoDB — sub-50ms scoring on 23 risk signals per transaction. Dashboard migration from legacy reports to Sigma Computing connected to Snowflake.

Data latency dropped from 12+ hours to under 5 minutes for analytics and sub-50ms for real-time fraud scoring. The pipeline processes 2.5 billion events daily at a cost of $4,200/month (vs. $18,000/month for the previous infrastructure). Complex risk queries that took 45+ seconds now complete in 1.8 seconds on Snowflake. Fraud detection caught $1.2M in attempted fraud in the first 90 days. Default rates decreased by 1.8 percentage points as analysts could act on fresh data. The client's 3-person internal team now focuses on analytics and ML model development rather than pipeline maintenance. Total project cost was approximately 70% less than the US-based consulting firm quotation.