Job Title: Data Quality Engineer / Data Testing Engineer – Snowflake & AWS Location: Jacksonville, FL Contract Required Qualifications: • Minimum 5 years of experience in data testing, ETL/ELT validation, or data quality engineering roles. • Strong SQL skills and experience testing in a Snowflake or similar cloud data warehouse environment. • Hands-on experience with AWS services (S3, Glue, Lambda) and data pipeline validation. • Experience with test automation frameworks and tools (e.g., PyTest, Great Expectations, dbt tests, or custom Python frameworks). • Solid understanding of data warehousing, data modeling, and pipeline orchestration. • Familiarity with Agile/Scrum development processes. • Excellent problem-solving skills, with strong attention to detail and data accuracy. Role Overview: • We are looking for a Data Quality Engineer with strong experience in testing data pipelines, validating data transformations, and ensuring data integrity across complex Snowflake and AWS-based data platforms. This role will involve creating automated data validation frameworks, developing test cases for ELT processes, and working closely with data engineers and business stakeholders to ensure high-quality, trusted data delivery. Key Responsibilities: • Design and implement robust data quality assurance frameworks for Snowflake-based ELT pipelines. • Create and maintain test cases, test data, and validation scripts to ensure data accuracy, completeness, consistency, and timeliness. • Validate data transformations, aggregations, and loading processes from various source systems to Snowflake. • Develop and execute automated data validation using SQL and scripting (e.g., Python). • Perform regression testing, data reconciliation, and row-level/aggregate-level validations across different stages of the pipeline. • Work with data engineers, business analysts, and stakeholders to define testing requirements and success criteria. • Participate in code reviews, Agile/Scrum ceremonies, and release validation activities. • Document all test scenarios, defects, and test results, and track through resolution. • Assist in monitoring production data pipelines to detect and address anomalies and data quality issues.