Data Quality Engineer

Job Summary:

Quality Engineer Data

Roles and Responsibilities:

• Design and implement QA strategies for data pipelines and ML models.
  
• Test data ingestion and streaming systems (StreamSets, Kafka) for accuracy and completeness.
  
• Validate data stored in MongoDB, ensuring schema and data integrity.
  
• Collaborate with Data Engineers to proactively address data quality issues.
  
• Work with Data Scientists to test and validate ML/DL/LLM/Agentic Workflow models.
  
• Automate data validation and model testing using tools like Pytest, Great Expectations, Deepchecks.
  
• Monitor production pipelines for data drift, model degradation, and performance issues.
  
• Participate in code reviews and create detailed QA documentation.
  
• Continuously improve QA processes based on industry best practices.
  

Required Technical Skills:

• 5 - 10 years of experience in QA, with focus on Data and ML testing.
  
• Proficient in SQL for complex data validation.
  
• Hands-on with StreamSets, Kafka, and MongoDB.
  
• Python scripting for test automation.
  
• Familiarity with ML model testing, metrics, and bias detection.
  
• Experience with cloud platforms (Azure, AWS, or GCP).
  
• Understanding of Node.js and React-based systems is a plus.
  
• Experience with QA tools like Pytest, Great Expectations, Deepchecks.
  

Additional Qualifications:

• Excellent communication and documentation skills.
  
• Strong analytical mindset and attention to detail.
  
• Experience working cross-functionally with Engineers, Scientists, and Product teams.
  
• Passion for learning new technologies and QA frameworks.
  
• Domain knowledge in facility management, IoT, or building automation is a plus.