Senior Data Quality Engineer

DescriptionJOB SUMMARY: The Senior Data Quality Engineer is responsible for validating, testing, and ensuring the reliability of data pipelines, ETL processes, and cloud-based data storage solutions. This role focuses on identifying and preventing data quality issues rather than building or engineering data solutions. The ideal candidate has a strong background in QA methodologies, SQL, Python for test automation, and data validation frameworks.Key Responsibilities: Design and execute test strategies to validate data integrity and ETL workflows. Develop and execute data validation and reconciliation test plans for large-scale data processing systems. Implement automated data quality checks using tools such as Great Expectations, DataGaps, or QuerySurge. Validate data pipelines, transformations, and aggregations within Azure Data Lake, Synapse, and Snowflake. Work closely with Data Engineers and BI teams to define data quality KPIs and monitoring strategies. Identify and document data discrepancies and quality issues for resolution by Data Engineers. Support CI/CD pipeline integration for automated data testing in DevOps environments. Monitor and enforce data governance and best practices. Maintain documentation of test plans, test cases, and results to ensure clarity and reproducibility.ADDITIONAL FUNCTIONS: Maintain a closed-loop defect reporting system. Prepare and maintain knowledge transfer documentation for onboarding new analysts and staff transitions. Mentor junior test engineers, guiding them in best practices and QA-focused problem-solving. Promote active participation in quality practice groups. Own and enhance best practices in test strategy, planning, and execution. Advocate for product integration improvements and a stronger Voice of the Customer focus.OTHER DUTIES: This job description is not exhaustive and may evolve based on business needs. Duties, responsibilities, and activities may change at any time with or without notice.RequirementsRequired Qualifications: Bachelor's degree in Computer Science, Data Science, or a related field. 5+ years of experience in data quality testing, ETL testing, and data validation. Expertise in SQL (T-SQL, PL/SQL) for querying, validating, and testing datasets. Strong experience with Python for data validation and test automation (Pandas, PySpark). Hands-on experience with data testing tools such as Great Expectations, DataGaps, QuerySurge, or Talend. Experience with Azure Data Lake, Azure Synapse, or other cloud-based data warehouses. Understanding of ETL workflows, data modeling, and data lineage tracking. Familiarity with CI/CD pipelines and DevOps practices for data testing. Excellent problem-solving, analytical, and communication skills.Preferred Qualifications: Experience with data governance frameworks such as Collibra or Apache Atlas. Knowledge of Apache Spark, Databricks, or Snowflake. Certification in Azure Data Engineer or AWS Big Data. Experience working in Agile and DevOps environments.Skills: Strong analytical and problem-solving abilities. Excellent leadership and mentoring skills. Strong communication (written and oral) in English. Ability to drive process improvement initiatives. Results-oriented with a "can-do" attitude-adaptability, flexibility, and resourcefulness.