Data QA Engineer

2 weeks ago

Bengaluru Gurugram Hyderabad, India Xebia It Architects Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Job Title : Data & AI-backed QA Engineer

We are seeking a highly skilled Automation, Data & AI-backed QA Engineer with 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 data- centric 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, self- healing 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 on- premise 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

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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