Data Sleek developed a ML-driven methodology to improve ETL processes by creating an automated anomaly detection system using Snowflake, machine learning, and time-series analysis. The system flagged anomalies with high accuracy, reducing manual oversight by 3 hours daily and refining predictions with user feedback. This enhanced system reliability, operational efficiency, and the ability to manage complex workflows.
“The ability to scale a team of data engineers and data scientists in a small amount of time was impressive. Data Sleek did a great job of onboarding each new candidate.”
Riti Chrea
CEO of Zappo Track
