
Quality Assurance Engineer
3 days ago
Description :
This position is with one of our clients in India, who is actively hiring candidates to expand their teams.
Title : Data & AI-backed QA Engineer.
Location : Bangalore, Bhopal, Jaipur, Gurgoan, Pune, and Chennai, Hyderabad.
Mode of Operation : Hybrid.
Employment Type : Permanent.
Role :
The ideal candidate will bring deep knowledge of test automation frameworks, strong data testing expertise, and innovative approaches leveraging AI/ML to enhance quality, coverage, and defect prediction.
Responsibilities :
- Design, develop, and maintain automation test frameworks for web, API, and datacentric applications.
- Perform data pipeline testing (ETL/ELT validation, schema verification, data integrity, reconciliation, and transformations across multiple sources/sinks).
- Implement AI/ML-powered testing solutions (e.g., intelligent test selection, selfhealing tests, defect clustering, log anomaly detection).
- Collaborate with engineering and data teams to ensure quality across data pipelines, APIs, and distributed systems.
- Conduct data validation testing for structured/unstructured data in cloud and onpremise environments.
- Integrate automation into CI/CD pipelines with robust monitoring and reporting.
- Analyze results, detect patterns using AI tools, and generate data-driven quality insights.
- Advocate modern QA practices (shift-left, continuous testing, risk-based prioritization).
Required Skills :
- 5 to 7 years of hands-on experience in automation testing.
- Good expertise in Selenium with Java or Python.
- Hands-on experience with Maven, TestNG, Cucumber, or equivalent frameworks.
- Proven experience in API Testing (Postman, RestAssured, etc.).
- Strong background in data testing, including : ETL/ELT, SQL queries, Data quality, large datasets validations.
ETL/ELT pipeline validation (batch & streaming).
- SQL queries for data verification and reconciliation.
- Data quality checks (completeness, accuracy, consistency, duplicates).
- Working with large datasets (RDBMS, data lakes, cloud warehouses such as Snowflake/BigQuery/Redshift).
- Familiarity with big data tools (Spark, Hadoop, Kafka) is a plus.
- Proficiency with CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps, etc.).
- Experience with AI-assisted testing approaches (self-healing, model-based testing, intelligent prioritization).
- Proficiency with version control (Git) and Agile tools (ADO/Jira).
- Strong debugging, analytical and risk mitigation skills.
- Excellent communication skills with ability to collaborate effectively.
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