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Brillio • Bangalore, Karnataka, India
Role & seniority: Lead Quality Engineer (Senior/Lead, Data Engineering QA leadership)
Stack/tools: ETL testing, SQL; Databricks testing; Azure (Databricks, Functions, Power BI); CI/CD (GitHub Actions); QA tooling: Jira, Confluence, SharePoint; AI-assisted test design
Design, develop, and maintain automated test frameworks; build reusable automated test suites for ETL, APIs, UI, and back-end systems
Plan, execute, and manage manual and automated tests across Dev/QA/Prod; track defects in Jira; ensure data integrity and coverage
Integrate test automation into CI/CD pipelines; lead the shift from manual QA to automation; support post-deployment validations and disaster recovery checks
Strong QA engineering background with test automation and CI/CD experience
Proficiency in SQL for data validation and automated testing in data platforms
Experience testing in Azure environments (Databricks, Azure Functions, Power BI)
Familiarity with Agile methodologies; hands-on with Jira, Confluence, Git, GitHub Actions, and SharePoint
Ability to design AI-assisted test cases and leverage AI for optimization
Experience setting up/optimizing CI/CD pipelines from scratch
Data security/compliance knowledge (PHI, data masking, access control)
Performance/load testing tools experience
Experience with AI/ML data science workflows
Location & work type: Location and work arrangement
Lead Quality Engineer \n
Primary Skills ETL Testing, SQL, Databricks testing.
Specialization
ETL Specialization: Senior Lead Data Engineer
Job requirements
We’re seeking an experienced and technically strong Quality Engineer (QE) to help transform our QA function. This is a hands-on role for professionals with a strong mix of data domain expertise, test automation experience, and CI/CD pipeline knowledge.
The QE will be instrumental in building scalable, automated test frameworks that support our cloud-first architecture on Azure, with heavy use of Databricks, Power BI, and Azure Functions.
The ideal candidate is comfortable working in a fast-paced, Agile environment and has experience across both data and software testing—with an eye toward continuous improvement and quality at scale.
Key Responsibilities Automation & QA Engineering
Design, develop, and maintain automated test frameworks.
Build reusable automated test suites for ETL pipelines, APIs, UI, and back-end systems. • Integrate tests into CI/CD pipelines using GitHub Actions.
Lead the effort to shift from manual QA to automation across platforms.
Test Planning & Execution
Create and manage test plans and cases using Jira, Confluence and SharePoint.
Execute both manual and automated tests across Dev, QA, and Prod environments.
Track, manage, and validate defects using Jira. Data Platform & Reporting Validation.
Perform deep data validations on Databricks, Power BI, and Azure-based data flows.
Collaborate with Data Engineers, Analysts, and BAs to ensure data integrity and test coverage.
Implement automated ETL validation to catch data quality issues early in the cycle. CI/CD Pipeline Integration
Set up and optimize CI/CD pipelines, ensuring test automation is fully integrated.
Participate in post-deployment validations and production readiness checks.
Support disaster recovery testing and environment stability checks. AI • Build and maintain AI-assisted automated test cases
Leverage AI capabilities for intelligent test case creation and optimization within Jira.
Required Skills & Experience
Strong QA engineering skills and experience, including test automation and CI/CD. • Strong knowledge of GitHub, GitHub Actions, and CI/CD practices.
Experience with SQL for data validation and automated testing in data platforms.
Proven experience testing in Azure environments (e.g., Databricks, Azure Functions, Power BI).
Familiarity with Agile methodologies and sprint-based development cycles.
Hands-on experience with AI, Jira, Confluence, Git, and SharePoint. Preferred Qualifications
Experience setting up CI/CD pipelines from scratch or optimizing existing workflows.
Background in data security and compliance (e.g., PHI, data masking, access control).
Experience with performance/load testing tools.
Knowledge of testing in AI/ML or data science workflows is a strong plus. Soft Skills
Strong communication and collaboration skills across technical and business teams.
Detail-oriented, with a mindset for continuous improvement and automation-first thinking.
Self-starter with the ability to manage time and tasks independently.
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