AWS Data Quality Engineer

Experience: 2–3 Years

Location: United States (Remote/Hybrid)

Important: This position supports programs subject to U.S. export control regulations (ITAR/EAR).
Candidates must be U.S. Citizens, U.S. Permanent Residents (Green Card Holders), or otherwise authorized to access export-controlled information under applicable U.S. regulations.

We are seeking a highly motivated AWS Data Quality Engineer to join our growing Data Engineering and Analytics team. In this role, you will be responsible for ensuring the accuracy, completeness, consistency, and reliability of data across enterprise data platforms. You will design and implement automated data validation frameworks, monitor data quality metrics, and proactively identify data anomalies across AWS-based data ecosystems.

The ideal candidate will have hands‑on experience with AWS data services, Python automation, SQL, and data quality frameworks, with a strong passion for building scalable testing solutions that support modern Data Ops and Analytics initiatives.

• Develop automated validation frameworks to verify source-to-target data movement across enterprise data pipelines.
• Create reusable Python-based testing utilities and automation scripts for validating schema consistency, completeness, accuracy, and timeliness of datasets.
• Design and maintain data reconciliation processes for large-scale structured and semi-structured datasets.
• Validate data pipelines built on AWS services including Amazon S3, AWS Glue, Amazon Redshift, and AWS Lambda.
• Perform end-to-end testing of ETL/ELT workflows and cloud-native data architectures.
• Ensure data transformation logic and business rules are correctly implemented throughout the data lifecycle.

• Implement continuous monitoring solutions for:
• Schema validation
• Data anomaly identification
• Build and maintain data quality dashboards, alerts, and operational reports.
• Establish proactive controls to detect and prevent data quality issues before they impact business stakeholders.

• Partner with Data Engineers, BI Developers, Data Analysts, and Application Teams to translate business requirements into measurable data quality KPIs and SLAs.
• Participate in Data Ops processes and CI/CD deployments to ensure quality checks are integrated into delivery pipelines.
• Support root-cause analysis and remediation of data quality issues.

• Analyze large-scale datasets using SQL and Python to identify inconsistencies and trends.
• Optimize validation frameworks for scalability and performance.
• Contribute to data governance and quality best practices across the organization.

• 2–3 years of experience in Data Engineering, Data Quality Engineering, ETL Testing, or related roles.
• Experience working with enterprise-scale cloud data platforms.

Strong hands‑on experience with:

• Amazon S3
• AWS Glue
• Amazon Redshift
• AWS Lambda
• Cloud Watch (preferred)
• Event Bridge / Step Functions (good to have)

• Advanced proficiency in Python.
• Experience building automation frameworks and validation utilities.
• Experience with Pandas, PySpark, or equivalent data processing libraries.

• Experience implementing data quality monitoring frameworks such as:
• Amazon Deequ
• Custom Python-based validation frameworks
• Knowledge of data profiling, reconciliation, and quality assurance methodologies.
• Experience with Apache Airflow or similar orchestration tools.
• Understanding of ETL/ELT architectures and data transformation processes.
• Experience working with batch and distributed data processing environments.

• Stron…