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A Data QA Engineer ensures data reliability and accuracy by testing data pipelines, ETL processes, and warehouses, focusing on completeness, consistency, and validity for business use, using tools like SQL and Python to build automated tests and find issues, crucial for trustworthy analytics and decisions. They work across the data lifecycle, defining quality rules, profiling data, and collaborating with data engineers and analysts to maintain high data integrity.
Job Responsibility:
Testing & Validation: Designing and running tests for data ingestion, transformation (ETL/ELT), and storage (data warehouses/lakes)
Data Profiling & Monitoring: Analyzing data structure, identifying anomalies, and setting up continuous monitoring for quality issues
Automation: Building automated test frameworks using Python, Pytest, and SQL to catch defects early
Rule Definition: Creating and implementing data quality rules and metrics (e.g., completeness, accuracy)
Collaboration: Working with data engineers, analysts, and business stakeholders to understand needs and resolve defects
Requirements:
SQL (complex queries)
Python (Pandas, PySpark)
Data Warehousing (DWH)
Cloud Platforms (AWS, Azure, GCP)
BI Tools (Tableau, Power BI)
Strong problem-solving
Attention to detail
Understanding data modeling, data lifecycle, and governance
Familiarity with data testing processes, data governance, and CI/CD