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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AWS Data Quality Engineer - **Company:** HCL America Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $59,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon S3, Data Analysis, Automation of Tests, Business Intelligence Development, Big Data, Computer Programming, Databases, Continuous Integration, Data as a Services, Data Validation, Data Control, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Data Profiling, Dataspaces, Data Warehousing, Database Queries, DevOps, Dimensional Modeling, Distributed Computing Environment, Python (Programming Language), E2e Testing, Cloud Services, DataOps, Shell Script, SQL Databases, Data Processing, Scripting, Great Expectations (Foster Youth College-readiness and Support Program), Delivery Pipeline, AWS Lambda, Pandas, Pyspark, AWS Glue, AWS Data Analytics, Cloudwatch, Data Pipelines, Amazon Redshift - **Published:** October 16, 2026 - **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-9C0DFE1170528A10FA/ ## About the Role Certification Preferred: AWS Certified Data Engineer - Associate 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 DataOps and Analytics initiatives., * 2-3 years of experience in Data Engineering, Data Quality Engineering, ETL Testing, or related roles. * Experience working with enterprise-scale cloud data platforms. Technical Skills AWS Strong hands-on experience with: * Amazon S3 * AWS Glue * Amazon Redshift * AWS Lambda * CloudWatch (preferred) * EventBridge/Step Functions (good to have) Programming & Automation * Advanced proficiency in Python. * Experience building automation frameworks and validation utilities. * Experience with Pandas, PySpark, or equivalent data processing libraries. Data Quality & Testing * Experience implementing data quality monitoring frameworks such as: * Amazon Deequ * Great Expectations * Custom Python-based validation frameworks * Knowledge of data profiling, reconciliation, and quality assurance methodologies. Data Engineering & Orchestration * 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. Database & Scripting * Strong SQL skills for data validation and analysis. * Experience with UNIX/Linux Shell scripting. * Understanding of data warehousing concepts and dimensional modeling., * AWS Certified Data Engineer - Associate (Highly Preferred). * Experience with DataOps/DevOps practices. * Knowledge of CI/CD pipelines and automated testing integration. * Experience with data observability platforms and monitoring solutions. * Exposure to Agile/Scrum delivery environments. Soft Skills * Excellent analytical and problem-solving capabilities. * Strong communication and stakeholder management skills. * Ability to work independently in a fast-paced environment. * Strong attention to detail and commitment to data accuracy. ## Description Data Quality Automation * 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. AWS Data Engineering & Validation * 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. Data Monitoring & Observability * Implement continuous monitoring solutions for: * Data drift detection * Schema validation * Data anomaly identification * Pipeline health monitoring * 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. Collaboration & Business Alignment * Partner with Data Engineers, BI Developers, Data Analysts, and Application Teams to translate business requirements into measurable data quality KPIs and SLAs. * Participate in DataOps processes and CI/CD deployments to ensure quality checks are integrated into delivery pipelines. * Support root-cause analysis and remediation of data quality issues. Performance & Optimization * 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.