Quality Engineer

We are looking for a highly motivated Senior Quality Engineer with strong expertise in automation and modern Quality Engineering practices. The ideal candidate will have hands-on experience on creating automated data validation scripts to test ETL data pipelines and report bugs or glitches. This position requires hands-on experience in ETL Testing and Automated Test Development. You will also be responsible for UI, API, backend, and performance testing, with a passion for engineering scalable automation solutions, driving quality throughout the SDLC, and leveraging AI-powered tools to improve software quality and delivery.

Key Responsibilities:

• Design and manage the data quality framework across production data pipelines.
• Build reusable data quality checks using tools such as Great Expectations or equivalent frameworks.
• Working with large and complex data sets (SQL and No-SQL), data lakes (S3) and data warehouses (Redshift).
• Test Automation - Build automated data validation scripts to test the quality of data in data lake (S3) and data warehouses (Redshift).
• 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 activities.
• Validate REST APIs, backend services, databases, and microservices through automated and manual testing.
• Perform performance and load testing using JMeter or BlazeMeter, analyse bottlenecks, and provide recommendations for performance improvements.
• Integrate automation suites into CI/CD pipelines using GitHub Actions, ensuring quality gates, automated reporting, and continuous feedback.
• Collaborate closely with Product Managers, Developers, DevOps, Business Analysts, and other stakeholders to ensure high-quality software delivery.
• Perform defect analysis, root cause analysis (RCA), and support production issue investigations.
• Leverage 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. Qualifications

Required Experience:

• 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.
• Minimum 2 year of hands-on coding experience in Python.
• Minimum 2 years of experience in ETL testing of Data Pipelines and Data Warehouse.
• Experience with cloud data lake technologies, including S3, Glue, Athena, Spark on EMR, and Apache Iceberg.
• Experience in API automation (REST APIs, Postman, 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 preferred).

Preferred Skills

• Mastery of specialized data quality tools (e.g., Great Expectations, Informatica DQ).
• Proficiency with cloud platforms and data warehouses (e.g., Databricks, Snowflake).
• Knowledge of global data privacy regulations (e.g., GDPR, CCPA) and best practices for data governance.
• Experience testing microservices-based and distributed architectures.
• Experience with monitoring and observability tools.
• Experience working on enterprise SaaS or publishing/content management platforms.

Nice to Have

• ISTQB Certification.
• Data Quality tool certified eg Informatica, Right Data etc
• AWS or Azure Fundamentals Certification.
• Familiarity with A/B testing frameworks and the nuances of experimentation
• Experience with AI-driven Quality Engineering practices.

What Were Looking For

• Strong ownership mindset with a quality-first approach.
• Strong ownership of data quality and reliability.
• Ability to identify and resolve the root cause of data quality issues.
• Good judgement on when a quality check should block, quarantine, or generate an alert.
• Ability to identify risks early and drive preventive quality practices.
• Quick learner with enthusiasm for adopting emerging technologies and AI-powered engineering solutions.
• Ability to mentor and coach team members while fostering a culture of quality.
• Strong collaboration skills with cross-functional teams and business stakeholders.
• Self-driven, proactive, and capable of working in a fast-paced Agile environment.
• Excellent communication, leadership, and stakeholder management skills.
  
  Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.