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Take2 Consulting, LLC • United States
Role & seniority: Data quality & testing engineer / senior data quality engineer (lead-oriented responsibilities)
Stack/tools: Microsoft Azure; data quality testing frameworks; SQL; Python/JavaScript/C#; data pipelines; cloud data warehouses; BI platforms; ML/AI model outputs; Agile, CI/CD, version control
Design, implement, and maintain automated data quality and testing frameworks for big data/analytics on Azure
Validate APIs, data pipelines, cloud warehouses, BI tools, and model outputs; establish data quality metrics, lineage, and anomaly detection
Integrate automated checks into Agile/CI-CD workflows; drive test planning, execution, governance, and continuous improvement
Advanced SQL and programming (Python/JavaScript/C#)
Experience with Azure data services and secure, scalable data solutions
Expertise in data quality, schema integrity, data lineage, anomaly detection, and model validation
Ability to define quality standards, acceptance criteria, and collaborate with data scientists, ML engineers, and product teams
Experience with statistical/analytical methods for validation
Familiarity with data governance practices, reproducibility, and version control best practices
Experience leading test planning across product lifecycles
Location & work type: Not specified in text; assume remote/hybrid or on-site as per company policy (note: confirm during interview)
Design and implement automated data quality and testing frameworks for big data and analytics platforms on Microsoft Azure. Validate APIs, data pipelines, cloud data warehouses, BI platforms, and ML/AI model outputs to ensure accuracy, reliability, and performance. Develop automated checks for data quality, schema integrity, lineage, business rules, and model validation using statistical and analytical methods. Establish and monitor data quality metrics, anomaly detection, and validation against training and test datasets. Integrate automated testing into Agile workflows and CI/CD pipelines, promoting best practices in version control, peer review, and reproducibility. Collaborate closely with data scientists, ML engineers, product teams, and stakeholders to define quality standards and acceptance criteria. Lead test planning and execution across the product lifecycle, championing data governance, transparency, and continuous improvement. Apply advanced SQL, Python/JavaScript/C#, and Azure data services expertise to ensure secure, scalable, and high-quality data solutions.