Data Quality Engineer

Job Description: Quality Engineer (Data)

JOB SUMMARY

We are seeking a highly skilled Quality Engineer with 5-10 years of professional experience to ensure the integrity, reliability, and performance of our data pipelines and AI/ML solutions within the SmartFM platform. The ideal candidate will be responsible for defining and implementing comprehensive quality assurance strategies for data ingestion, transformation, storage, and the machine learning models that generate insights from alarms and notifications received from various building devices. This role is crucial in delivering high-quality, trustworthy data and intelligent recommendations to optimize facility operations.

ROLES AND RESPONSIBILITIES

• Develop and implement end-to-end quality assurance strategies and test plans for data pipelines, data transformations, and machine learning models within the SmartFM platform.
  
• Design, develop, and execute test cases for data ingestion processes, ensuring data completeness, consistency, and accuracy from various sources, especially those flowing through IBM StreamSets and Kafka.
  
• Perform rigorous data validation and quality checks on data stored in MongoDB, including schema validation, data integrity checks, and performance testing of data retrieval.
  
• Collaborate closely with Data Engineers to ensure the robustness and scalability of data pipelines and to identify and resolve data quality issues at their source.
  
• Work with Data Scientists to validate the performance, accuracy, fairness, and robustness of Machine Learning, Deep Learning, Agentic Workflows, and LLM-based models. This includes testing model predictions, evaluating metrics, and identifying potential biases.
  
• Implement automated testing frameworks for data quality, pipeline validation, and model performance monitoring.
  
• Monitor production data pipelines and deployed models for data drift, concept drift, and performance degradation, setting up appropriate alerts and reporting mechanisms.
  
• Participate in code reviews for data engineering and data science components, ensuring adherence to quality standards and best practices.
  
• Document testing procedures, test results, and data quality metrics, providing clear and actionable insights to cross-functional teams.
  
• Stay updated with the latest trends and tools in data quality assurance, big data testing, and MLOps, advocating for continuous improvement in our quality processes.
  

REQUIRED TECHNICAL SKILLS AND EXPERIENCE

• 5-10 years of professional experience in Quality Assurance, with a significant focus on data quality, big data testing, or ML model testing.
  
• Strong proficiency in SQL for complex data validation, querying, and analysis across large datasets.
  
• Hands-on experience with data pipeline technologies like IBM StreamSets and Apache Kafka.
  
• Proven experience in testing and validating data stored in MongoDB or similar NoSQL databases.
  
• Proficiency in Python for scripting, test automation, and data validation.
  
• Familiarity with Machine Learning and Deep Learning concepts, including model evaluation metrics, bias detection, and performance testing.
  
• Understanding of Agentic Workflows and LLMs from a testing perspective, including prompt validation and output quality assessment.
  
• Experience with cloud platforms (Azure, AWS, or GCP) and their data/ML services.
  
• Knowledge of automated testing frameworks and tools relevant to data and ML (e.g., Pytest, Great Expectations, Deepchecks).
  
• Familiarity with and React environments to understand system integration points.
  

ADDITIONAL QUALIFICATIONS

• Demonstrated expertise in written and verbal communication, adept at simplifying complex technical concepts related to data quality and model performance for diverse audiences.
  
• Exceptional problem-solving and analytical skills with a keen eye for detail in data.
  
• Experienced in collaborating seamlessly with Data Engineers, Data Scientists, Software Engineers, and Product Managers.
  
• Highly motivated to acquire new skills, explore emerging technologies in data quality and AI/ML testing, and stay updated on the latest industry best practices.
  
• Domain knowledge in facility management, IoT, or building automation is a plus.
  

EDUCATION REQUIREMENTS / EXPERIENCE

• Bachelor's (BE / BTech) / Master's degree (MS/MTech) in Computer Science, Information Systems, Engineering, Statistics, or a related field.