Quality Engineer

RightData, Inc.

India

On-site

INR 800,000 - 1,400,000

Full time

46 hours ago

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Benefits offered by this job

Competitive salary & bonus structure

Corporate medical cover

14 days paid holidays + holidays

Wiki-Wednesdays

Wellness sessions & Friday-Fundays

Quarterly team building days

Job summary

RightData, Inc. is seeking a Quality Engineer in India to own end-to-end feature testing with a focus on user experience, data quality, and edge cases. You’ll explore UI, APIs, backend services, databases, data/metadata, and AI components to uncover issues before customers are affected.

You will design tests from requirements, collaborate with Product and Engineering, and leverage AI for data generation and script creation. 3–5 years of QA experience is expected.

Qualifications

3–5 years of QA / Quality Engineering experience.
Strong SQL with joins, subqueries and data validation.
Experience with defect tracking (JIRA / Azure DevOps) and Agile.
Familiarity with AI-assisted QA and testing of AI/LLM applications.
Exposure to Python/test scripts and automation is a plus.

Responsibilities

Translate requirements into comprehensive test scenarios and user journeys.
Test UI, API, backend, and data flows end-to-end.
Collaborate with Product and Engineering to report defects and observations.
Design load, performance, and regression tests.
Use AI tools to generate test data and cases.
Monitor product quality across data, metadata and AI components.

Skills

QA / Quality Engineering

Test design

SQL

Agile

AI-assisted QA

Debugging

Defect management

Performance testing

Jira / Azure DevOps

Education

B.Tech./M.Tech./MCA

Tools

Selenium

Playwright

JMeter

ETL

Job description

India 3-5 years Full-time QA Notice period: Immediate – 30 Days

About the Role

We are looking for a Quality Engineer who can independently drive feature testing end-to-end, with a strong focus on user experience, data quality and real-world edge cases. The ideal candidate should be comfortable understanding flows across UI, APIs, backend services, databases, data and metadata layers, and AI/LLM components. They should be curious, analytical, willing to continuously upskill, and comfortable challenging requirements and assumptions to uncover issues before they impact customers.

Key Responsibilities

Product & Test Design: Understand requirements and translate them into comprehensive test scenarios and user journeys
Identify gaps, ambiguities, negative scenarios, boundary conditions and edge cases
Think from the end-user perspective and simulate realistic business scenarios
Proactively explore beyond documented or discussed requirements to uncover unexpected product behaviour
Product & Technical Understanding: Develop a strong understanding of how the product works across UI, APIs, backend services, databases, data/metadata and Python/AI components
Understand dependencies and application flow sufficiently to trace issues and identify their likely source
Collaborate with Product and Engineering teams to communicate defects, impact and technical observations
Data & Metadata Understanding: Understand fundamental concepts of data, metadata, lineage, classification and governance
Work with different databases and data sources, including structured, semi-structured and unstructured data
Use SQL to validate data accuracy, completeness, consistency and relationships
Test scenarios involving different data structures, schema changes, metadata inconsistencies, relationships and classifications
Quality, Performance & Release Validation: Execute functional, integration, exploratory and regression testing
Validate product behaviour under large data volumes, concurrency and long-running processes
Design and execute load, stress and scalability tests
Investigate failures using application behaviour, logs and data, and provide root-cause insights
Support release validation and post-deployment verification
AI-Driven QA: Use AI throughout the QA lifecycle to understand requirements, identify scenarios, research solutions, generate test data, create scripts and analyse failures
Use AI to accelerate performance/stress testing, data generation and test coverage
Continuously identify opportunities to make QA more efficient and comprehensive through AI

Requirements

3–5 years of QA / Quality Engineering experience
Test Design: Ability to design comprehensive test scenarios covering positive, negative, boundary and real-world edge cases
Databases & SQL: Working knowledge of relational databases and SQL, including joins, subqueries, aggregations, filtering and data comparison
Data & Metadata: Ability to work with structured, semi-structured and unstructured data across different data sources, with an understanding of metadata, relationships, lineage, classification and governance
Performance Testing: Experience testing large datasets, concurrent users and metadata-intensive operations to measure throughput, response time and system stability
Debugging: Ability to read application logs, interpret errors and isolate defects
Defect Management: Experience with JIRA, Azure DevOps or equivalent
Agile: Experience working in sprint-based development and release cycles
AI-Assisted QA: Use GPT, Claude and other AI tools to create user flows, test cases, test data, Python/test scripts and performance scripts, and to analyse logs and defects

Nice to Have