Data Engineer

Trilon is building a supercharged, technology-enabled future for our people and partners. The Data Engineer plays a key role in that mission by building and maintaining the data platform that powers Trilon’s enterprise analytics, automation, and AI capabilities. Reporting to the Vice President, Data & DevOps, this role is responsible for designing, developing, and maintaining scalable data integrations and transformations in Azure and Microsoft Fabric. The Data Engineer ensures that Trilon’s data platform delivers reliable, high-quality, and well-structured data to support business intelligence, operations, and innovation. This role serves as the primary custodian of Trilon’s integrated data model and is instrumental in developing a unified, extensible architecture that scales with continued acquisitions. The Data Engineer designs and builds secure Power BI semantic models for consumption by analysts and decision-makers, ensuring consistent and governed access to enterprise data. This role also partners closely with the AI and Innovation vTeam to prepare data for analytics, machine learning, and retrieval-augmented generation (RAG) applications. Key Responsibilities

Data Platform Engineering and Maintenance

- Serve as the primary owner and technical steward of the Trilon enterprise data platform

- Design, develop, and maintain data pipelines and workflows using Azure Data Factory, Synapse, and Microsoft Fabric

- Build and manage data transformations, orchestration, and automation across structured, semi-structured, and unstructured data sources

- Ensure scalability, reliability, and performance of the data platform as Trilon continues to grow through acquisition

- Implement monitoring and alerting to proactively detect and resolve pipeline or data quality issues

Data Integration and Modeling

- Develop and maintain integrations between Trilon’s enterprise systems, cloud services, and acquired partner environments

- Design and maintain a unified, scalable data model that harmonizes data across business systems

- Build secure, governed, and high-performance Power BI semantic models optimized for analytics and self-service reporting

- Collaborate with business analysts and data consumers to ensure data models support enterprise reporting needs and KPIs

- Partner with cybersecurity and infrastructure teams to ensure data models and access patterns meet compliance and governance standards

Data Quality and Governance

- Implement validation and quality checks to ensure accuracy, completeness, and timeliness of enterprise data sets

- Maintain metadata, lineage, and documentation to promote transparency and reusability

- Define and enforce data quality and consistency standards across all integrated sources

- Collaborate with the Technology Asset Manager and Service Platform Manager to align system integrations and data governance

- Support data cataloging, discovery, and classification initiatives within Microsoft Purview or equivalent tools

Automation, Optimization, and Resilience

- Develop automated frameworks for ingestion, transformation, and validation using Azure-native tools and pipelines

- Implement DevOps principles for data workflows including version control, testing, and deployment automation

- Optimize pipeline performance, resource utilization, and data freshness

- Build resilience and fault tolerance into data operations to ensure reliability and recovery

- Create reusable components and templates to streamline integration of new data sources and partner systems

AI and Innovation Enablement

- Collaborate with the AI and Innovation vTeam to prepare and structure data for AI, ML, and RAG-based applications

- Develop and maintain data pipelines that support model training, evaluation, and fine-tuning

- Curate and transform unstructured data for retrieval, embedding, and vectorization within AI applications

- Ensure data readiness for generative AI tools, chat int