Data Quality EngineerDepartment: IT
Employment Type: Full Time
Location: Remote- USA
DescriptionThe Data Quality Engineer plays a critical role in that mission by ensuring the data that powers every AI and digital tool is accurate, consistent, complete, and trustworthy.
This role owns data quality across the entire Data Platform, defining what good data looks like and ensuring that standard is enforced in practice. You are responsible for building the validation, monitoring, and observability systems that detect issues early and prevent bad data from reaching downstream tools where it erodes trust and usability.
You maintain and evolve the enterprise data quality rubric, treating it as a living standard that governs how data is measured and evaluated across all pipelines and domains. You score data quality on a regular cadence and provide clear visibility into platform health, giving leadership an accurate view of where data is strong and where it needs improvement.
You work closely with Data Engineers to embed quality checks into every pipeline, and with product and AI teams to understand how data quality issues surface in real tools. When issues arise, you trace them back to the source and resolve them at the pipeline level.
This role requires strong data engineering fundamentals, experience building data quality frameworks, and a systematic approach to defining and enforcing standards. You are detail-oriented, structured in your thinking, and motivated by building a data foundation that engineers trust.
Key ResponsibilitiesData Quality Framework and Standards
- Define, maintain, and evolve the enterprise data quality rubric across all data domains
- Establish standards for data accuracy, completeness, consistency, timeliness, and reliability
- Ensure data quality expectations are clearly defined and consistently applied across the platform
- Govern how data quality is measured, scored, and reported
Validation and Monitoring
- Design and implement automated data quality checks within data pipelines
- Build validation rules that detect anomalies, schema drift, missing data, and inconsistencies
- Ensure issues are identified at the source before propagating downstream
- Continuously improve validation coverage and effectiveness
Data Observability and Pipeline Health
- Build and maintain observability systems for pipeline health, data freshness, and performance
- Monitor data flows for failures, delays, and unexpected changes
- Provide visibility into pipeline status and data quality metrics across the platform
- Implement alerting and reporting mechanisms for critical issues
Issue Investigation and Resolution
- Diagnose data quality issues and trace them back to source systems or pipeline logic
- Partner with Data Engineers to resolve issues at the pipeline level
- Work with product and AI teams to understand how data issues impact tool behavior
- Ensure root causes are addressed and not repeated
Cross-Functional Collaboration
- Work with the Lead Data Engineer to align on pipeline architecture and quality standards
- Partner with pod Data Engineers to embed quality checks into all pipelines
- Collaborate with Lead Engineers and Applied AI Engineers to understand downstream impacts
- Communicate data quality insights clearly to both technical teams and leadership
Reporting and Continuous Improvement
- Score and report on data quality across the platform on a defined cadence
- Provide leadership with a clear view of data health, risks, and improvement areas
- Identify systemic issues and drive improvements in data processes and standards
- Continuously refine data quality practices as the platform evolves
Skills, Knowledge and Expertise- Experience designing scalable technical architectures for AI or machine learning solutions in enterprise environments
- Strong understanding of large language models, vector databases, embeddings, prompt orchestration, and model serving
- Hands-on experience with Azure services including Azure OpenAI, Azure Machine Learning, and Azure Functions
- Familiarity with LLM frameworks and orchestration tools such as LangChain, Semantic Kernel, or custom agent frameworks
- Knowledge of enterprise security, responsible AI principles, and compliance frameworks such as GDPR and CCPA
- Proven ability to create architecture documentation and communicate effectively with technical and non-technical audiences
- Experience integrating AI solutions into platforms such as Power Platform, SharePoint, and Microsoft Teams
- Bachelor's or master's degree in computer science, data science, engineering, or related field
- Certifications in cloud architecture or AI/ML disciplines preferred
