01Overview
Key Responsibilities
Design, develop, and execute comprehensive test plans, test cases, and test scripts for ETL and data engineering applications.
Validate ETL workflows by ensuring data accuracy, completeness, consistency, and integrity across source and target systems.
Perform testing on large-scale data processing applications built using Apache Spark.
Develop and maintain automated test scripts using Python.
Execute data validation by writing complex SQL queries and comparing source and target datasets.
Identify, analyze, and report defects while working closely with development and data engineering teams for resolution.
Validate data transformations, business rules, and end-to-end data migration processes.
Participate in requirement analysis, test planning, and defect triage meetings.
Ensure adherence to QA best practices, testing standards, and release timelines.
Support regression, integration, system, and performance testing activities.
Required Skills
Strong hands-on experience in ETL Testing.
Mandatory experience with Python programming.
Strong experience with Apache Spark (Spark SQL/ PySpark).
Excellent SQL skills for data validation and reconciliation.
Experience testing data warehouses and large-scale data processing systems.
Good understanding of data transformation, data mapping, and ETL workflows.
Experience with defect tracking and test management tools such as JIRA, ALM, or Azure DevOps.
Familiarity with Agile/Scrum development methodologies.
Strong analytical, debugging, and problem-solving skills.
Good to Have
Experience with Hadoop ecosystem (Hive, HDFS, Impala).
Exposure to cloud platforms such as AWS, Azure, or GCP.
Knowledge of CI/CD pipelines and test automation frameworks.
Experience with data quality tools and API testing.
Banking or Financial Services domain experience. .
