Data Test Automation Engineer

Job Title

Location:

Experience Required:

Joining Requirement:

on or before 10th September

Role Overview

Software Development Engineer in Test (SDET)

Quality Engineering

Data Engineering

Key Responsibilities

• Design, develop, and execute automated test cases for data pipelines and integration flows.
• Perform ETL testing with a strong emphasis on data quality, transformation logic, and validation rules.
• Build and maintain automated frameworks using Selenium with Java for UI and backend validation.
• Conduct thorough API testing using REST protocols for services validation.
• Validate streaming data and message workflows, particularly using Kafka or similar message brokers.
• Perform structured and unstructured data validation using NoSQL databases such as Cassandra or MongoDB.
• Work with OpenSearch/Elasticsearch platforms to validate data indexing, queries, and relevance scoring.
• Participate in code reviews, defect triage, and cross-functional coordination with data engineers, developers, and DevOps teams.
• Implement test cases in CI/CD pipelines ensuring continuous quality in agile deployments.
  

Required Skills & Qualifications

• Strong ETL Automation Testing experience – must be clearly demonstrated in prior projects.
• Proficient in Selenium with Java for functional and regression test automation.
• Solid experience in REST API testing and related tools.
• Hands-on experience in at least two of the following:
• Kafka or similar queuing systems
• NoSQL databases (e.g., Cassandra, MongoDB)
• OpenSearch or Elasticsearch
  

• Strong understanding of test automation frameworks, data validation strategies, and CI/CD practices.
• Excellent analytical and debugging skills.
  

Preferred Attributes

• Exposure to working with data-centric products or in data-first organizations.
• Strong communication skills and the ability to collaborate with cross-functional teams and stakeholders.
• Self-driven with a continuous improvement mindset and a passion for automation and data accuracy.
  

Interview Process

• Round 1: Internal Screening
• Round 2 & 3: Technical Interviews with Engineering Stakeholders
  

Work Mode

Hybrid Model