Senior Engineer

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Key Responsibilities:

6+ years of experience in Pythin, ETL and AWS Design and implement data testing strategies for ETL processes, data transformations, and data validation across various data domains.
Develop and execute test plans, test cases, and test scripts to validate data flows, data quality, and data accuracy. Perform data reconciliation between source and target systems to ensure data integrity.
Analyze complex datasets and validate data mappings, business logic, and data models. Collaborate with Data Engineering and Development teams to resolve data quality issues and ensure data accuracy across data pipelines.
Support the testing of data models, data warehouses, and reporting solutions, ensuring alignment with business requirements. Execute regression testing, defect management, and root cause analysis for data-related issues.
Participate in discussions on future state architecture and provide inputs on data testing best practices.
Develop SQL queries for data extraction, validation, and comparison across multiple databases. Implement data testing automation using Python scripting and other data validation tools. Required Skills and Experience:
5+ years of experience in data testing, ETL testing, and data validation.
Strong proficiency in SQL for data querying, data comparison, and data integrity validation. Hands-on experience with data integration tools like SSIS and Fivetran.
Experience in data modeling and working with data warehouses. Strong analytical skills to work with large datasets, data mapping, and data transformation logic.
Basic scripting skills in Python for data manipulation and testing (not full programming).
Exposure to emerging data engineering tools such as dbt, Databricks, Delta Lake, Iceberg, ADLS, and Data Virtualization (preferred).
Experience with data testing frameworks and data validation tools like DataGap, Query surge, and Tricentis (preferred), PySpark, etc.
Familiarity with testing concepts like regression testing, risk-based testing, and defect lifecycle management.
Strong communication skills to work effectively with cross-functional teams in a global delivery model. Ability to work in a dynamic environment with evolving data platforms and architecture.
Preferred Qualifications:
Previous experience in banking, finance, or similar data-centric industries. Understanding of AI/ML concepts and their application in data testing (preferred). Knowledge of data warehousing principles and best practices.

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