1. Data Testing and Validation: Develop comprehensive test plans and execute them to ensure data integrity, accuracy, and consistency throughout the data processing lifecycle. Collaborate with the Data Engineer Team to identify data quality issues and drive their resolution.
2. SQL Proficiency: Utilize your strong SQL skills and in-depth understanding of business logic to create complex queries for data validation, verification, and error detection.
3. Python Automation: Leverage your expertise in Python automation to build and maintain testing frameworks, automation scripts, and data validation tools. Automate repetitive tasks to streamline the testing process and improve team productivity.
4. End-to-End Testing: Perform end-to-end testing of data pipelines, data transformations, and ETL workflows to validate data flow and identify potential bottlenecks or issues.
5. Documentation: Create and maintain detailed documentation for test cases, test scenarios, and test results. Document data anomalies, data discrepancies, and potential improvements in data processing.
6. Collaborate with Data Engineers: Work seamlessly with the Data Engineer Team to understand data processing workflows and architecture. Participate in code reviews and provide feedback to improve code quality and maintainability.
7. Continuous Improvement: Proactively identify opportunities for process improvement, testing automation, and data quality enhancements. Implement best practices and industry standards to ensure data quality and reliability.

1. Minimum 3 years of experience in Quality Assurance (QA) with a focus on data testing, preferably in a Data Engineer Team or similar role.
2. Strong SQL skills and a deep understanding of data structures, data models, and business logic, with a minimum of 3 years of hands-on experience writing SQL queries.
3. Proficient in Python programming, with at least 2 years of experience in developing automation scripts for data testing and validation.
4. Knowledge of data processing methodologies, data ingestion, and data architecture.
5. Analytical mindset and a keen eye for detail to identify data quality issues and troubleshoot data-related problems effectively.
6. Excellent communication skills, both written and verbal, with the ability to collaborate with cross-functional teams.
7. Proven ability to work in a fast-paced, agile environment and manage multiple priorities effectively.

1. Bachelor's degree in Computer Science / Industrial Engineering / Information Systems or a related field is preferred.
2. Familiarity with the technology stack used by the Data Engineer Team, including dbt, Snowflake (data warehouse), Apache Airflow (as an orchestrator), nifi, and Kafka.

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