OverviewIn 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.

ResponsabilidadesAnalyze 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 principalesBachelor'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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