Trilon is building a supercharged, technology-enabled future for our people and partners. The 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 Responsibilities

Data 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, o

Data Quality Engineer at Trilon Group