Position:
Experience Level:
ABOUT THE ROLE
We are seeking a highly skilled Database Testing & ETL QA Engineer with expertise in warehouse data pipelines and ETL processes. The ideal candidate will have strong automation skills, data validation expertise, and experience in testing complex ETL workflows. You will be responsible for designing, executing, and automating test strategies to ensure high-quality delivery of data integrity and pipeline reliability across all warehouse stages.
KEY RESPONSIBILITIES
- Execute testing activities across all stages of the Kalidowarehouse ETL pipeline, including data extraction, transformation, loading, and validation.
- Design and develop comprehensive ETL test strategies, including data validation, reconciliation, and end-to-end pipeline testing.
- Create detailed test plans, test cases, and scripts based on ETL requirements, data schemas, and business logic.
- Develop and maintain automation frameworks for ETL testing using industry-standard tools and scripting languages.
- Automate end-to-end testing, including functional validation, regression testing, and data quality checks across warehouse stages.
- Validate data flow and transformation logic at each stage of the ETL pipeline, ensuring data accuracy and completeness.
- Perform data reconciliation between source systems, staging areas, and target warehouse tables using SQL queries and data comparison tools.
- Analyze complex data requirements and ETL logic to identify gaps, data quality issues, and suggest improvements.
- Troubleshoot and diagnose ETL failures, working closely with development and data engineering teams to identify root causes and drive resolutions.
- Support continuous integration and deployment pipelines, integrating automated ETL tests within CI/CD workflows using tools like Azure DevOps or Jenkins.
- Log, track, and manage defects using tools like Jira or Azure DevOps, ensuring timely resolution and traceability.
- Monitor testing metrics and data quality indicators, continuously improving test processes, automation frameworks, and best practices.
- Assess the impact of schema changes and pipeline modifications, defining regression test suites accordingly.
- Share knowledge through documentation and knowledge-sharing sessions.
MUST-HAVE SKILLS
- 3-7 years of hands-on experience in database testing, ETL testing, or data warehouse validation.
- Excellent proficiency in SQL writing complex queries for data validation, reconciliation, ETL verification, and advanced data analysis.
- Advanced proficiency in Excel – including pivot tables, VLOOKUP, data manipulation, and advanced formulas for data validation and reporting.
- Experience with Kalidowarehouse or similar data warehouse platforms (Snowflake, Teradata, Oracle Data, etc.).
- Hands-on experience in designing and executing comprehensive test plans, including functional, regression, and data quality testing.
- Knowledge of ETL tools and concepts – understanding of ETL workflows, data pipelines, staging areas, and warehouse architecture.
- Experience in test automation using industry-standard tools for ETL validation.
- Expertise in data validation techniques – reconciliation, data profiling, completeness checks, accuracy validation, and referential integrity testing.
- Familiarity with test management and defect tracking tools such as Jira, Azure DevOps, or similar platforms.
- Ability to create and maintain test data, understand database schemas, and analyze application architecture and data flow.
- Knowledge of CI/CD pipelines and automation integration, including Azure DevOps or Jenkins.
- Good analytical, troubleshooting, and problem-solving skills with attention to detail.
- Understanding of SAVI/AMI database project context or similar financial/insurance data systems is a plus.
ADDITIONAL SKILLS & QUALITIES
- Strong knowledge of ETL best practices and data warehouse testing methodologies.
- Ability to interpret complex data sets and metrics to optimize testing strategies and identify data quality issues.
- Experience working in Agile/Scrum teams and with version control systems (Git).
- Excellent communication and collaboration skills.
- Proactive approach to identifying data quality risks and recommending preventive measures.
- Python programming knowledge is an advantage for advanced test automation and data manipulation.
- Database certifications (e.g., SQL Server, Oracle, Snowflake) are a plus.
- Domain knowledge in insurance, banking, or financial data systems is advantageous.
