Quality Engineer

Informa Group .

3d•Hybrid

About The Position

Taylor & Francis is seeking a highly motivated Senior Quality Engineer with strong expertise in automation and modern Quality Engineering practices. The role focuses on testing ETL data pipelines and developing automated data validation scripts, while also covering UI, API, backend, and performance testing. The successful candidate will help build scalable automation solutions, promote quality throughout the SDLC, and use AI-powered tools to improve software quality and delivery. The engineer will design and manage data quality frameworks for production pipelines, work with large SQL and NoSQL datasets, data lakes, and data warehouses, and integrate checks throughout data ingestion, transformation, and publishing. They will contribute to test planning and release validation, investigate defects and production issues, and collaborate with product, development, DevOps, business analysis, and other teams. The role also includes mentoring junior Quality Engineers and improving automation coverage, framework stability, and engineering practices. The position uses a balanced working model. Candidates must have the right to work and live in India and live within commuting distance of the Bengaluru office. There is an expectation of at least three days per week in person, with flexibility to work from home or remotely at other times.

Requirements

3–4 years of experience in Quality Engineering, with strong expertise in Data Engineering, Data Quality, or Data Reliability Engineering.
Hands-on experience with data quality frameworks such as Great Expectations, Soda, dbt Tests, Deequ, or equivalent tools.
Strong SQL skills, including window functions, complex joins, and queries on large partitioned datasets.
At least 2 years of hands-on coding experience in Python.
At least 2 years of experience in ETL testing of data pipelines and data warehouses.
Experience with cloud data lake technologies, including S3, Glue, Athena, Spark on EMR, and Apache Iceberg.
Experience in API automation, including REST APIs, Postman, and API testing frameworks.
Experience integrating automated testing into CI/CD pipelines using GitHub Actions.
Strong understanding of SDLC, STLC, Agile/Scrum, and modern Quality Engineering practices.
Experience with Git and collaborative development workflows.
Strong understanding of functional, integration, regression, system, and end-to-end testing.
Experience with test strategy, risk-based testing, test planning, and quality metrics.
Experience in defect management, root cause analysis, and release quality validation.
Strong analytical, debugging, and problem-solving skills.
Excellent communication, collaboration, and stakeholder management skills.
Experience using AI-assisted testing tools to improve test design, automation development, execution, and maintenance; Claude Code is preferred.
Strong ownership mindset and quality-first approach, including ownership of data quality and reliability.
Ability to identify and resolve root causes of data quality issues and decide when checks should block, quarantine, or generate alerts.
Ability to identify risks early and drive preventive quality practices.
Quick learner with enthusiasm for emerging technologies and AI-powered engineering solutions.
Ability to mentor and coach team members and foster a culture of quality.
Self-driven, proactive, and capable of working in a fast-paced Agile environment.
Candidates must have the right to work and live in India and live within commuting distance of the Bengaluru office.

Nice To Haves

Mastery of specialized data quality tools such as Great Expectations or Informatica DQ.
Proficiency with cloud platforms and data warehouses such as Databricks or Snowflake.
Knowledge of data privacy regulations such as GDPR and CCPA, and data governance best practices.
Experience testing microservices-based and distributed architectures.
Experience with monitoring and observability tools.
Experience working on enterprise SaaS or publishing/content management platforms.
ISTQB certification.
Data quality tool certification, such as Informatica or Right Data.
AWS or Azure Fundamentals certification.
Familiarity with A/B testing frameworks and experimentation.
Experience with AI-driven Quality Engineering practices.

Responsibilities

Design and manage the data quality framework across production data pipelines.
Build reusable data quality checks using Great Expectations or equivalent frameworks.
Work with large and complex SQL and NoSQL datasets, S3 data lakes, and Redshift data warehouses.
Build automated data validation scripts for data in S3 data lakes and Redshift data warehouses.
Define checks for schema, null values, uniqueness, referential integrity, data volume, freshness, late-arriving data, and business rules.
Integrate data quality checks at ingestion, transformation, and pre-publish stages.
Participate in requirements analysis, test strategy, test planning, risk assessment, test estimation, and release validation.
Validate REST APIs, backend services, databases, and microservices through automated and manual testing.
Perform performance and load testing using JMeter or BlazeMeter, analyze bottlenecks, and recommend performance improvements.
Integrate automation suites into CI/CD pipelines using GitHub Actions, including quality gates, automated reporting, and continuous feedback.
Collaborate with Product Managers, Developers, DevOps, Business Analysts, and other stakeholders to support high-quality software delivery.
Perform defect analysis and root cause analysis, and support production issue investigations.
Use AI-powered engineering tools such as Claude Code to accelerate test design, automation development, defect analysis, and framework maintenance.
Continuously improve automation coverage, framework stability, execution efficiency, and engineering best practices.
Mentor junior Quality Engineers through technical guidance, code reviews, and knowledge sharing.