ETL Tester - Python

Description : We are looking for a Senior ETL Tester with 6+ years of experience who views data quality through the lens of a software engineer. In this role, you will move beyond manual spreadsheet comparisons to design, build, and maintain robust, automated testing frameworks that ensure the absolute integrity, performance, and reliability of our data pipelines.You will act as the ultimate gatekeeper of data quality. Working alongside Data Engineers and Analytics teams, you will leverage Python and advanced SQL to validate complex ELT/ETL transformations, verify large-scale cloud data migrations, and profile data structures. If you are passionate about treating data quality as code and implementing automated data assurance at scale, this role is for you.Requirements : - Experience : 6+ years of hands-on experience in Data Quality Assurance, ETL Testing, or Data Analytics Engineering.- Python Mastery for Testing : Proven track record of using Python to manipulate data (e.g., Pandas), call APIs, parse files (JSON, CSV, Parquet), and build automated test scripts.- Expert-Level SQL : Exceptional ability to write, debug, and optimize complex SQL queries for massive datasets in modern cloud data warehouses.- Data Architecture Knowledge : Strong conceptual understanding of data warehousing concepts, including Dimensional Modeling, Slowly Changing Dimensions (SCD), and ETL architectures.- Agile Mindset : Comfortable operating in fast-moving, Agile team setups, utilizing Jira for sprint planning and Git for version control.- Communication Skills : Ability to clearly explain data discrepancies, pinpoint root causes within complex pipeline steps, and present data-driven quality metrics to technical and business stakeholders.Job Responsibilities : - Test Automation & Frameworks : Design, develop, and scale end-to-end automated testing frameworks using Python and Pytest to validate data ingestion, transformation, and storage processes.- ETL/ELT Validation : Write complex SQL scripts to verify data completeness, correctness, transformations, and business logic mapping between source systems and target data warehouses.- Data Profiling & Quality : Implement data quality gates, schema validation checks, and data profiling metrics using programmatic tools (e.g., Great Expectations).- Regression & Performance Testing : Conduct performance, scalability, and stress testing on data pipelines to identify bottlenecks in query execution and data throughput.- CI/CD Integration : Embed automated data quality tests into CI/CD pipelines (e.g., GitHub Actions, GitLab CI/CD) and workflow orchestrators (e.g., Airflow, dbt) to catch data issues before they hit production.- Collaboration & Defect Management : Partner closely with Data Engineers, Product Managers, and BI teams to analyze data requirements, trace data lineage, and log/track software bugs and data anomalies. (ref:hirist.tech)