Key Responsibilities:

Quality Engineering & Test Automation:

- Define and execute QA strategies for cloud-native applications, APIs, data platforms, and analytics solutions.
- Design, develop, and maintain automated UI, API, integration, and end-to-end test frameworks. Integrate automated testing into CI/CD pipelines and release processes. Drive test planning, defect management, release validation, and quality reporting.

Data Platform Testing:

- Validate ETL/ELT pipelines, data transformations, and large-scale analytical datasets across cloud environments. Verify data quality rules, observability metrics, dashboards, alerts, and reporting outputs. Perform data validation, reconciliation, and root cause analysis across enterprise data platforms.

Cloud, API & AI Testing:

- Test REST APIs, microservices, event-driven architectures, and cloud-native applications. Validate AI-enabled capabilities, anomaly detection workflows, LLM-powered solutions, and intelligent automation features. Support performance, security, scalability, and reliability testing across the platform.

Engineering Excellence:

- Champion automation-first and shift-left testing practices. Collaborate with engineering teams on quality standards, testability, and continuous improvement. Mentor junior QA engineers and promote quality best practices across Agile teams. Participate in architecture and design discussions and adhere to Agile/Scrum best practices.

Key Requirements:

- 6-8 years of Quality Engineering experience in enterprise software, data platforms, or cloud-native applications.
- Advanced SQL and Python (PyTest, Pandas) skills for data validation, reconciliation, and test automation.
- Strong experience testing modern frontend and backend applications built using React/Angular,



TypeScript, JavaScript (ES6+), HTML5/CSS3, Python, FastAPI, or Flask.
- Strong experience validating ETL/ELT pipelines, data transformations, and large-scale analytical datasets.
- Solid understanding of data quality dimensions (completeness, accuracy, consistency, timeliness, uniqueness, and validity).
- Experience with data quality/testing frameworks such as Great Expectations, Soda, dbt Tests, or equivalent.
- Solid understanding of Airflow DAGs, batch/streaming pipelines, idempotency, backfills, and late-arriving data scenarios.
- Hands-on experience with test automation tools such as Playwright, Selenium, Cypress, Rest Assured, Postman, and PyTest.
- Experience testing web applications, REST APIs, microservices, distributed systems, and data integrations.
- Experience with GCP (BigQuery, Airflow, Cloud Storage), databases (PostgreSQL, MySQL, MongoDB), and modern DevOps practices (CI/CD, Git).
- Strong understanding of functional, regression, integration, system, performance, and security testing.
- Excellent problem-solving, analytical, communication, and stakeholder management skills.
- Demonstrated ability to lead QA workstreams and mentor junior QA engineers to achieve quality and delivery objectives.

Preferred Qualifications:

- Experience with Data Quality, Data Governance, Data Observability, or Analytics platforms.
- Knowledge of data quality frameworks, rule-based validation, metadata-driven systems, and monitoring solutions.
- Experience testing dashboards and reporting platforms such as Power BI or Tableau.
- Exposure to anomaly detection using LLM-enabled methods, or intelligent automation solutions.
- Experience using AI-assisted engineering tools such as GitHub Copilot, Gemini, Claude, Cursor, or similar platforms.

📌 Automation Test Engineer (Hyderabad)
🏢 Dun & Bradstreet
📍 Hyderabad