Data Analyst Expert

About the role

Join our client to design and implement an AI‑driven validation platform on AWS that ensures data pipeline quality. You will blend data engineering, machine learning, and generative AI to detect anomalies, reconcile source and target data, and provide actionable insights before issues reach downstream consumers.

Key responsibilities

• Analyze existing ETL validation processes and identify automation opportunities.
• Develop AI‑driven validation frameworks and anomaly detection models for continuous data quality monitoring.
• Automate record count, completeness, and consistency checks across pipelines.
• Generate AI‑assisted validation reports that explain detected issues in clear, actionable terms.
• Integrate validation capabilities into AWS services such as Glue, Lambda, Redshift, Athena, and S3.
• Collaborate with data engineering, QA, and business teams to define data quality rules, thresholds, and SLAs.

Required profile

• 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.
• Practical experience with machine learning and anomaly detection techniques applied to data.
• Solid understanding of data quality and governance principles.

Required skills

• Python
• SQL
• Amazon Bedrock
• AWS Lambda
• AWS Glue
• Amazon Redshift
• Amazon Athena
• Amazon S3
• Machine learning
• Anomaly detection