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Remote, common QA title and mid-level (4+ yrs) attract many applicants despite niche ML/data skills.
Skills transferable across data/AI teams but require specific Azure Databricks and ML validation experience.
Explicit 4+ years and mandatory Azure/Databricks, SQL, Python, and data-testing skills create strict screening.
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Own quality assurance, data integrity, and validation of ETL/ELT pipelines and forecasting models within Azure Databricks and Azure AI Foundry.
Design and execute automated test plans for data pipelines and machine learning model outputs, ensuring accuracy, completeness, and anomaly detection in forecasting data.
Integrate data quality checks and validation into CI/CD pipelines and monitor model/data drift in production environments.
4+ years experience in QA Engineering, Data Validation, or Big Data Testing with exposure to Machine Learning or Predictive Analytics platforms.
Proficient in Python for data validation and expert-level SQL for data profiling and auditing.
Hands-on experience with Azure Databricks using PySpark or SparkSQL and familiarity with Azure AI Foundry or MLflow.
Experience with data quality frameworks such as Great Expectations or similar.
Experienced in validating complex data pipelines and predictive analytics, especially time-series/demand forecasting models.
Comfortable operating at the intersection of Big Data, Cloud infrastructure (Azure), and advanced AI/ML workflows.
Skilled in developing automated validation frameworks integrated into CI/CD using DevOps pipelines.