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.
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.
Preferred Qualifications:
Experience with CI/CD testing in data environments.
Knowledge of data profiling, anomaly detection, and data observability tools.
Exposure to BI testing (e.g., validating dashboards, reports).
Experience working with dbt, Airflow, or similar orchestration tools.
Background in regulated industries such as finance, healthcare, or insurance.
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