Job Purpose
Design and execute data validation and testing workflows across front‑end integrations, backend/middle‑tier services, and cloud‑based data pipelines. Support the translation of post‑trade or financial data flows into clear test scenarios, data validation rules, and regression test suites. Develop Python‑based scripts for test automation, data comparison, and validation activities. Collaborate with engineering teams to ensure accurate execution of batch jobs, stored procedures, and CI/CD‑driven testing processes across environments.
The ideal candidate brings foundational data engineering testing experience, strong analytical skills, basic exposure to Capital Markets (good to have), and hands‑on ability to use Python for scripting and validation tasks.
Desired Skills And Experience
5-7 years of hands‑on experience in Python doing data validation or data engineering testing, including writing test cases and executing regression tests.
Strong experience using Python for scripting, data validation, and automation; familiarity with libraries like pandas. Experience with Python foundational frameworks and libraries is a must.
Good understanding of SQL, including writing queries to validate datasets, compare results, and support DB‑level testing.
Experience working with different data sources such as Streams, Files and databases; Handling of each of these data sources and their validations in Python is highly desirable.
Exposure to stored procedures and ability to validate logic and outputs across environments (SQL Server/Oracle/Postgres preferred).
Familiarity with CI/CD tools (e.g., Azure DevOps, Jenkins, or GitLab) and an understanding of Git for version control.
Good analytical skills to understand post‑trade or financial datasets and translate them into repeatable validation scenarios (Capital Markets domain knowledge is good to have).
Experience working with test case management tools (e.g., JIRA, XRay) and following agile development practices.
Ability to contribute to QA processes, including manual testing, data validation, and regression testing for critical applications.
Strong attention to detail, good communication skills, and a willingness to learn and grow within a data engineering/testing environment.
Key Responsibilities
- Support the creation and execution of Python-based test scripts for data validation, regression checks, and automation tasks.
- Perform data validation and comparison across datasets/environments, identifying mismatches and documenting results.
- Write and execute SQL queries to validate database outputs, stored procedures, and transformations across environments (Dev/UAT/Prod).
- Assist in testing backend services, APIs, and data workflows, ensuring accuracy, reliability, and consistency of data.
- Read and understand stored procedure logic to help create corresponding test cases and automated validation scripts.
- Participate in defining test scenarios for post‑trade or financial data flows (allocations, settlements, confirmations, etc.), where applicable.
- Contribute to regression test packs by maintaining a repository of reusable checks and validation rules.
- Support CI/CD‑driven testing processes by running automated tests and validating results during deployments.
- Work with job schedulers like AutoSys/Airflow (if applicable) to execute batch processes and validate outcomes.
- Document test results, defects, and validation logic clearly; collaborate with developers and data engineers to troubleshoot issues.
- Follow established QA processes and help identify opportunities to improve test coverage and efficiency.
- Coordinate with cross‑functional teams to ensure smooth test execution and adherence to quality expectations.
Behavioral Competencies
Soft Skills:
- Excellent problem-solving, analytical, and communication skills (written and verbal).
- Strong organizational abilities with meticulous attention to detail.
- Ability to work independently with minimal supervision and collaboratively in cross-functional teams.
