The company had a working product but no real data infrastructure. Application databases were being queried directly for analytics, slowing down production systems. The team wanted to add AI features but had no reliable pipeline to feed models with clean, current data.
Designed and deployed a cloud data platform with event-driven ingestion, a staging layer for raw data, and a curated analytics warehouse. Built incremental pipelines that kept the warehouse in sync without impacting production databases. Added data quality checks and alerting so the team could trust what they were building on.
Decoupled analytics from production, eliminating performance issues. Gave the data science team a reliable, fresh dataset to train and validate models against, cutting their feature engineering cycle from weeks to days.
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