Job description_

About the Role_

Join our client and help bring AI-driven quality control to the data pipelines that power their business. 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 experience).
• 5+ years of experience in data engineering, ETL development, or data quality roles.
• Strong programming skills in Python and advanced SQL.
• Hands-on experience with Amazon Bedrock and building generative AI solutions (prompt design, LLM-based analysis and reporting).
• Hands-on experience with AWS Lambda, AWS Glue, Amazon Redshift, Amazon Athena, and Amazon S3.
• Practical experience with machine learning and anomaly detection techniques applied to data (statistical, time-series, or ML-based).
• Solid understanding of data quality and data governance principles (completeness, accuracy, consistency, lineage).
• Experience designing source-to-target reconciliation and ETL testing strategies.
• Solid understanding of Agile, SDLC, and modern engineering best practices.

Desired or nice to have expertise_

• Experience with data quality frameworks such as AWS Glue Data Quality, Great Expectations, or Deequ.
• Experience building monitoring dashboards (Amazon QuickSight, CloudWatch, or similar).
• Familiarity with Amazon SageMaker or other ML platforms for model training and deployment.
• Exposure to agentic AI workflows and AI-assisted development tools (e.g., GitHub Copilot, Claude Code, Cursor).
• AWS certifications (e.g., Data Engineer – Associate, Machine Learning – Specialty)

Our Core values are focus to inclusion and diversity, all qualified applicants will be considered for employment and will go thru a fair recruitment process regardless of their race, religion, gender identity, sexual orientation, national origin or disability status.

Your journey with us begins here!!