Job Summary:
We are seeking a skilled and detail-oriented Data Quality Engineer to join our data engineering and analytics team. The ideal candidate will be responsible for ensuring the accuracy, consistency, and reliability of data pipelines, data transformations, and analytical datasets across enterprise environments. You will develop automated testing frameworks, validate ETL processes, and collaborate with cross-functional teams including Data Engineers, Cloud Engineers, and BI Analysts to maintain high data quality standards. This role requires strong technical expertise in data platforms, scripting, testing tools, and cloud-based data integration workflows.
Key Responsibilities:
Develop and implement automated testing frameworks for ETL, data pipelines, and data processing workflows.
Perform data validation and integrity checks across data warehouses, data lakes, and streaming data systems.
Create and maintain automated test cases using scripting languages and testing frameworks (e.g., PySpark PyTest, DBT, API testing tools).
Collaborate with Data Engineering teams to identify, troubleshoot, and resolve data quality issues.
Execute performance and load testing activities using tools such as JMeter and LoadRunner.
Work with event-driven streaming platforms (e.g., Kafka, Event Hubs) to validate real-time data pipelines.
Support CI/CD pipelines and integrate testing processes using Git-based version control and DevOps tools.
Document test strategies, test results, and data quality processes.
Perform other duties as assigned.
Qualifications:
Must-Have:
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
5+ years of experience in software quality engineering with at least 3 years in an enterprise data environment.
Proven experience in automated testing tools such as PySpark PyTest, DBT, ETL automation tools, and API automation tools.
Hands-on experience with ETL automation platforms such as DBT and Airflow.
Strong understanding of data management, data warehousing, data lakes, and cloud-based integration workflows.
Experience with event-driven data streaming platforms such as Kafka or Event Hubs.
Proficiency in SQL and scripting languages (e.g., Python) for test automation and data validation.
Familiarity with performance and load testing tools such as JMeter and LoadRunner.
Experience working with Azure Data Factory, Azure Synapse Analytics, or similar Azure-based data integration tools.
Knowledge of Agile, Scrum, and CI/CD development methodologies.
Hands-on experience with version control systems (e.g., Git) and CI/CD tools (e.g., Jenkins, Azure DevOps).
Strong analytical, communication, collaboration, and problem-solving skills.
Good-to-Have:
Experience with Microsoft Fabric.
Experience working with data governance tools such as Azure Purview.
Relevant certifications such as ISTQB, Azure DevOps, Azure ML, or similar credentials.
In Brief:
Title: Data Quality Engineer
Qualification: Bachelor’s degree in a relevant technical field with strong experience in automated testing of enterprise data systems.
Experience Needed: Minimum 5+ years
No. of Openings: 01
Joining Type: Immediate / with-in 15 Days