SDET II (ETL Testing)

We are looking for a highly skilled SDET 2 - ETL Tester to join our Data Engineering team. In this role, you will validate data pipelines, ETL processes, and data warehouse systems through scalable automation frameworks. You will work closely with data engineers, backend engineers, and analytics teams to ensure data accuracy, integrity, and reliability across large-scale data systems.

The core responsibilities for the job include the following:

ETL and Data Validation:

• Validate data transformations across ETL/ELT pipelines.
• Perform source-to-target data validation.
• Write complex SQL queries for reconciliation and data analysis.
• Validate large datasets across data warehouses and data lakes.
• Perform data quality checks, including nulls, duplicates, schema validation, and referential integrity.

Automation and Framework Development:

• Build and maintain automated data validation frameworks.
• Automate regression testing for data pipelines.
• Integrate ETL test suites into CI/CD pipelines.
• Develop reusable testing utilities to reduce manual validation efforts.

Pipeline and Workflow Testing:

• Test batch and real-time data pipelines.
• Validate workflows in orchestration tools such as Airflow.
• Perform incremental load and Change Data Capture (CDC) validation.
• Ensure API-to-database data consistency.

Quality and Performance:

• Perform performance testing on large datasets.
• Identify data inconsistencies and conduct root cause analysis.
• Ensure adherence to data governance and compliance standards.
• Maintain test documentation, reports, and quality dashboards.

Requirements:

• 2-6 years of experience in ETL/data testing or a related SDET role.
• Strong SQL skills, including joins, aggregations, and window functions.
• Experience working with data warehouses such as Snowflake, Redshift, or BigQuery.
• Hands-on experience with ETL tools such as Informatica, Talend, or dbt.
• Programming experience in Python or Java.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI.
• Strong understanding of data modeling concepts, including fact/dimension models and star schemas.
• Experience testing large-scale datasets with millions or more records.

Preferred Qualifications:

• Experience with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of big data technologies such as Spark or Hadoop.
• Experience with orchestration tools such as Airflow.
• Familiarity with data quality tools such as Great Expectations or Deequ.
• Exposure to BI tools such as Tableau or Power BI and experience validating BI data.