Data & AI-backed QA Engineer

Job Description – Data & AI-backed QA Engineer (5–7 years)

We are seeking a highly skilled Automation, Data & AI-backed QA Engineer with 5–7 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

▪ 5–7 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 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.