Senior Engineer, Quality Assurance

ROLE DESCRIPTION SUMMARY

Should be able to ensure accuracy, reliability, performance, and quality of our data pipelines. Should be able to work closely with data engineers, analysts, and product teams to validate data workflows, test ETL/ELT pipelines and develop automated testing solutions that maintain trust in our ecosystem.

PRIMARY RESPONSIBILITIES / KEY RESULT AREAS

• Validate the data pipelines (ELT/ETL) for correctness, completeness, and performance across batch and real-time systems.
• Develop and execute test plans, test cases, and test scripts for data pipelines.
• Perform functional, regression, integration, and end-to-end testing for data engineering solutions.
• Conduct data validations including schema checks, boundary checks, transformations, record-level validations.
• Ensure data integrity between source, intermediate tables, warehouses, and downstream systems.
• Build automated scripts for data pipelines using frameworks like great expectations, dbt tests.
• Use (Snowflake) SQL extensively to query, validate, and profile datasets.

COMPETENCIES

• Collaborate with data engineers to identify issues, define acceptance criteria, and improve pipeline reliability.
• Document test strategies, quality metrics, and data validation procedures.
• Participate in design reviews and provide QA perspective on new data feature or pipelines.
• Establish data quality metrics (accuracy, completeness, freshness, timeliness).
• Ensure compliance with data governance, data security, and audit requirements.

QUALIFICATIONS & EXPERIENCE

• Bachelor’s degree in computer science, Data Engineering, Information Systems or related field.
• 5+ years of experience in QA or data analytics.
• 3+ years of experience with QA Automation.
• Strong SQL skills for data validation and troubleshooting.
• Experience testing ETL/ELT processes in Snowflake.
• Familiarity with workflow orchestration tools like Airflow.
• Hands on experience with test automation frameworks for data pipelines.
• Understanding of data warehousing concepts.
• Exposure to cloud platform AWS.