Data & AI-backed QA Engineer [Apply in 3 Minutes]
1 week ago
Job Description
Job Description Data & AI-backed QA Engineer (57 years)
We are seeking a highly skilled Automation, Data & AI-backed QA Engineer with 57 years of
proven expertise in automation testing, data pipeline validation, and AI-driven quality
assurance practices. 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.
Key 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 & Experience
57 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 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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