Responsibilities

Design and manage data quality frameworks across production data pipelines.
Build reusable data quality checks and automated validation scripts for S3 data lakes and Redshift warehouses.
Validate data schemas, 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.
Plan and execute requirements analysis, test strategy, test planning, risk assessment, test estimation, and release validation.
Perform automated and manual testing of REST APIs, backend services, databases, and microservices.
Conduct performance and load testing using JMeter or BlazeMeter, analyze bottlenecks, and recommend improvements.
Integrate automation suites into GitHub Actions with quality gates, automated reporting, and continuous feedback.
Perform defect analysis, root cause analysis, and production issue investigations.
Use AI-assisted engineering tools to accelerate test design, automation development, defect analysis, and framework maintenance.
Improve automation coverage, framework stability, execution efficiency, and quality engineering practices.
Mentor junior Quality Engineers through technical guidance, code reviews, and knowledge sharing.

Requirements

3–4 years of experience in Quality Engineering, 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 two years of hands-on Python coding experience.
At least two years of experience testing ETL data pipelines and data warehouses.
Experience with S3, Glue, Athena, Spark on EMR, and Apache Iceberg.
Experience with REST API automation, Postman, and API testing frameworks.
Experience integrating automated testing into CI/CD pipelines using GitHub Actions.
Understanding of SDLC, STLC, Agile/Scrum, functional, integration, regression, system, and end-to-end testing.
Experience with test strategy, risk-based testing, test planning, quality metrics, defect management, root cause analysis, and release validation.
Strong analytical, debugging, problem-solving, communication, collaboration, and stakeholder management skills.
Preferred experience with Informatica DQ, Databricks, Snowflake, data privacy regulations, data governance, monitoring and observability tools, distributed architectures, enterprise SaaS, or publishing platforms.
ISTQB, data quality tool, AWS, or Azure Fundamentals certification is desirable.
Familiarity with A/B testing frameworks and AI-driven Quality Engineering practices is desirable.
Must have the right to work and live in India and be able to commute to the Bengaluru office.

Benefits

Competitive salary and bonus.
24 days of annual leave.
Four volunteering days annually and a day off for the employee’s birthday.
Pension contributions, medical insurance for the employee and dependants, life cover, and personal accident cover.
Seasonal social and charitable events.
Training and development opportunities.
Tools and equipment for remote working.
Balanced hybrid working model with at least three days per week in person and flexibility to work from home or remotely otherwise.
Flexible adjustments are available during the recruitment process for candidates who need them.

Quality Engineer

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