Description

You design, develop, and execute automated test cases for ETL processes and data pipeline validation.

This role is hybrid.

Responsibilities

• Validate extraction, transformation, and loading (ETL) processes across multiple data sources and targets for data accuracy and completeness.
• Perform data reconciliation, data quality checks, and verification of business rules using complex SQL queries.
• Develop, enhance, and maintain automation frameworks leveraging Python and relevant testing tools.
• Validate big data transformations in PySpark within distributed environments and test data flows in data warehouses and data lakes.
• Identify, troubleshoot, and analyze defects, collaborating with development teams for resolution and root cause analysis.

Required Skills

• 8+ years of experience in ETL/data testing, automation testing, or related roles.
• Expert-level proficiency in SQL for complex queries, joins, aggregation, and performance optimization.
• Hands-on experience with Python for test automation, scripting, and data validation.
• Working knowledge of PySpark for validating transformations in big data environments.
• Deep understanding of data warehouse concepts, including fact/dimension tables, star/snowflake schemas, SCDs, and data modeling.
• Strong domain experience in the financial industry (banking, capital markets, risk management, trading, payments, or regulatory reporting).
• Proven experience testing batch and near-real-time data pipelines.
• Experience working in Agile/Scrum environments.

Preferred Skills

• Hands-on experience with cloud data platforms such as AWS, Azure, or GCP.
• Knowledge of data platform tools like Databricks and Snowflake.
• Exposure to CI/CD pipelines and integration of automated tests within deployment workflows.

Key Skills

Education

Bachelor's degree