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 ResponsibilitiesData Quality Framework and StandardsDefine, maintain, and evolve the enterprise data quality rubric across all data domainsEstablish standards for data accuracy, completeness, consistency, timeliness, and reliabilityEnsure data quality expectations are clearly defined and consistently applied across the platformGovern how data quality is measured, scored, and reportedValidation and MonitoringDesign and implement automated data quality checks within data pipelinesBuild validation rules that detect anomalies, schema drift, missing data, and inconsistenciesEnsure issues are identified at the source before propagating downstreamContinuously improve validation coverage and effectivenessData Observability and Pipeline HealthBuild and maintain observability systems for pipeline health, data freshness, and performanceMonitor data flows for failures, delays, and unexpected changesProvide visibility into pipeline status and data quality metrics across the platformImplement alerting and reporting mechanisms for critical issuesIssue Investigation and ResolutionDiagnose data quality issues and trace them back to source systems or pipeline logicPartner with Data Engineers to resolve issues at the pipeline levelWork with product and AI teams to understand how data issues impact tool behaviorEnsure root causes are addressed and not repeatedCross-Functional CollaborationWork with the Lead Data Engineer to align on pipeline architecture and quality standardsPartner with pod Data Engineers to embed quality checks into all pipelinesCollaborate with Lead Engineers and Applied AI Engineers to understand downstream impactsCommunicate data quality insights clearly to both technical teams and leadershipReporting and Continuous ImprovementScore and report on data quality across the platform on a defined cadenceProvide leadership with a clear view of data health, risks, and improvement areasIdentify systemic issues and drive improvements in data processes and standardsContinuously refine data quality practices as the platform evolvesSkills, Knowledge and ExpertiseExperience designing scalable technical architectures for AI or machine learning solutions in enterprise environmentsStrong understanding of large language models, vector databases, embeddings, prompt orchestration, and model servingHands-on experience with Azure services including Azure OpenAI, Azure Machine Learning, and Azure FunctionsFamiliarity with LLM frameworks and orchestration tools such as LangChain, Semantic Kernel, or custom agent frameworksKnowledge of enterprise security, responsible AI principles, and compliance frameworks such as GDPR and CCPAProven ability to create architecture documentation and communicate effectively with technical and non-technical audiencesExperience integrating AI solutions into platforms such as Power Platform, SharePoint, and Microsoft TeamsBachelor’s or master’s degree in computer science, data science, engineering, or related fieldCertifications in cloud architecture or AI/ML disciplines preferredAbout TrilonTrilon was formed with the vision of building the next Top 20 infrastructure consulting firm in North America by bringing together some of the nation’s best infrastructure consulting firms, focused on delivering practical and sustainable infrastructure solutions. Trilon is backed by Alpine Investors, a PeopleFirst Private Equity Firm. Trilon currently comprises 5,500+ staff across the US. For more information, visit www.trilongroup.com.About Trilon GroupTrilon group was formed with a vision to build the next Top 20 design firm in North America with a reputation for delivering smart and sustainable infrastructure solutions. Our investment in talent ensures we are the most trusted partner by our clients, our talent, and our investors. Similar to the infrastructure we design, we want to build an enduring company for our clients, our people, and the communities we serve. We invest in partners who ensure infrastructure solutions address some of the communities most complex challenges of sustainability, resiliency, social equity, and constructibility. As a People First company, we are focused on growing the careers of our people faster within the Trilon group than our peers. We invest heavily in developing and elevating talent across our family of companies. Trilon Group offers a multitude of career paths, spanning technical, project management, business management, business development, and operations.
