Data & AI-backed QA Engineer

K&K Talents

Title: Data & AI-backed QA Engineer

Location: Bangalore, Bhopal, Jaipur, Gurgoan, Pune, and Chennai, Hyderabad

Mode of Operation: Hybrid

Employment Type: Permanent

Role:

Automation, Data & AI-backed QA Engineer

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:

• 5–7 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.