DataOps Framework with Quality Monitoring

The Problem

Data quality issues were reaching production, impacting stakeholder trust. The team lacked visibility into pipeline health and had no automated rollback capabilities.

My Approach

I implemented a comprehensive DataOps framework with CI/CD-driven deployments, automated testing, and real-time quality monitoring. Built dashboards for ops visibility.

What I Built

• Automated data quality checks using Python and SQL
• CI/CD pipeline with staged deployments (dev → staging → prod)
• Comprehensive test coverage for data transformations
• Real-time monitoring dashboards tracking pipeline health
• Automated alerting for data anomalies and pipeline failures
• Rollback procedures for failed deployments

Impact & Results