Data QA Test Engineer

Job Description Data QA Engineer (2–3 Years, Python + SQL + Cloud)

Position Overview

We are seeking a Data QA Engineer with 2–3 years of experience in Python, SQL, and cloud-based ETL/data testing. The role focuses on validating data pipelines, ensuring data quality, and building Python scripts for automation. Basic knowledge of ML and GenAI models is a plus.

Key Responsibilities

• Validate ETL processes in AWS, Azure, GCP, or Snowflake.
• Perform checks on schema, row counts, duplicates, nulls, and aggregates.
• Reconcile source and target datasets.
• Write SQL queries (joins, subqueries, CTEs, window functions) for testing.
• Perform performance checks and data consistency validation.
• Write validation scripts using pandas, numpy, sqlalchemy.
• Automate test cases with pytest or unittest.
• Validate data from cloud storage (S3, Blob, GCS) using Python SDKs.
• Apply data quality checks with frameworks like Great Expectations.
• Assist in validating ML workflows (data preparation, input/output checks).
• Work with Python libraries (scikit-learn, tensorflow) for basic model testing.
• Explore GenAI APIs (OpenAI, HuggingFace) for test data generation.

Required Skills

• 2–3 years of experience in data QA/testing.
• Strong knowledge of Python (pandas, numpy, pytest, sqlalchemy).
• Proficiency in SQL (joins, aggregates, window functions).
• Hands-on with at least one cloud platform (AWS, Azure, GCP, Snowflake, Databricks).
• Understanding of data quality checks (row counts, duplicates, transformations).

Good to Have

• Experience with ML/GenAI concepts and Python libraries.
• Familiarity with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps).
• Knowledge of workflow tools like Airflow or Prefect.
• Basic API testing using Python (requests).

Qualifications

• Bachelor’s in Computer Science, IT, or related field.
• 2–3 years of experience in ETL/Data QA with Python + SQL.
• Exposure to cloud platforms and data pipelines.