Overview

In this role you will design and implement an AI-powered data validation platform for ETL pipelines on AWS, ensuring data quality and governance. You will work with cross-functional teams to automate validation, detect anomalies, and explain issues before they affect downstream systems. The role combines data engineering, ML-driven validation, and generative AI to scale quality checks across workloads. You will shape the validation roadmap and contribute to data quality strategy while delivering actionable insights.

Responsabilidades

- 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)
- Collaborate with data engineering, QA, and business teams to define data quality rules, thresholds, and SLAs




- Roadmap for expanding AI-driven validation across additional workloads

Requisitos principales

- 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

- collaboration with cross-functional teams
- clear communication of complex concepts
- problem solving and initiative
- Python
- SQL
- Amazon Bedrock

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