Job description_About the Role_Join our client and help bring AI-driven quality control to the data pipelines that power their business.
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You'll analyze how ETL processes are validated today and design an AI-powered validation platform on AWS that detects anomalies, reconciles source and target data, and explains issues before they reach downstream consumers.
This role blends data engineering, machine learning / anomaly detection, and generative AI (Amazon Bedrock), giving you the opportunity to shape both the data quality and governance strategy and the roadmap for AI-driven validation across additional workloads. A day in this role_Analyze existing ETL validation processes and identify automation opportunities.
Develop AI-driven validation frameworks for data pipelines.
Implement anomaly detection for continuous data quality monitoring.
Compare source data against ETL outputs to identify and track discrepancies.




Automate record count, completeness, and consistency checks across pipelines.
Generate AI-assisted validation reports that explain detected issues in clear, actionable terms.
Integrate AI validation capabilities into AWS data platforms and workflows (Glue, Lambda, Redshift, Athena, S3).Evaluate how AI can be used to deliver in-depth QA analysis of deliverables.
Collaborate with data engineering, QA, and business teams to define data quality rules, thresholds, and SLAs. AI-powered data validation platform.
Automated ETL quality control framework.
Anomaly detection models and monitoring dashboards.
Validation reports and root-cause analysis capabilities.
Data quality KPIs and governance recommendations.
Roadmap for expanding AI-driven validation across additional workloads.
The expertise requested_Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or related field (or equivalent ex

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