AWS Lakehouse Data Quality Assurance Testing Lead

About The Role

You will make an impact by driving data quality, integrity, and reliability across enterprise-scale AWS Lakehouse platforms. You will be a valued member of the Data & Analytics team and work collaboratively with data engineers, architects, business stakeholders, and quality engineering teams to ensure the successful delivery of high-quality, trusted data products that support critical business decisions.

Responsibilities

• Lead end-to-end data quality assurance and validation activities across AWS Lakehouse environments, ensuring accuracy, completeness, consistency, and reliability of enterprise data assets.
• Design and implement scalable testing frameworks, automation strategies, and quality controls for ETL/ELT pipelines, data transformations, and analytical workloads.
• Validate data movement and processing across bronze, silver, and gold layers, ensuring compliance with established data governance and quality standards.
• Partner with cross-functional teams to identify, troubleshoot, and resolve data quality issues while driving continuous process improvements.
• Provide technical leadership and mentorship to quality engineering teams, promoting best practices in data testing, automation, and cloud-native quality assurance.

Requirements

• 10+ years of experience in data warehousing, data engineering, data quality assurance, or data platform testing.
• Strong experience validating cloud-based data platforms, data lakes, data warehouses, and modern Lakehouse architectures.
• Hands-on expertise with SQL and Python for data validation, reconciliation, test automation, and quality assurance activities.
• Experience testing ETL/ELT pipelines, data transformations, metadata, data lineage, schema evolution, and large-scale analytical datasets.
• Knowledge of AWS data services, including Amazon S3, AWS Glue, Amazon Athena, and related cloud-native data technologies.
• Experience implementing data quality frameworks and automated testing solutions within enterprise environments.
• Strong understanding of data governance, data quality standards, regulatory compliance, and audit requirements.
• Excellent problem-solving, stakeholder management, communication, and leadership skills.

Qualifications

• Experience working with Apache Iceberg tables, Snowflake, DBT, Spark, and Airflow in large-scale data ecosystems.
• Knowledge of CI/CD practices and automated quality assurance within cloud-native delivery pipelines.
• Experience supporting enterprise data lake, data warehouse, or analytics modernization initiatives.
• Familiarity with performance testing, workload optimization, and data platform scalability assessments.
• Experience leading distributed teams and managing data quality programs across multiple delivery workstreams.
• Knowledge of Agile delivery methodologies, including Scrum, Kanban, and DevOps-based operating models.