Data EngineerDepartment: IT
Employment Type: Full Time
Location: Remote- USA
Compensation: $116,000 - $155,000 / year
DescriptionThe 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 ResponsibilitiesData 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 interfaces, and knowledge retrieval systems
- Stay informed of emerging AI data engineering trends and Microsoft Fabric AI integrations
Collaboration and Cross-Domain Partnership
- Partner with application and infrastructure teams to ensure reliable and secure data exchange across systems
- Collaborate with business stakeholders and analysts to understand reporting needs and deliver usable data models
- Support integration engineers in onboarding new firms and ensuring their data aligns with Trilon's enterprise model
- Work closely with cybersecurity and compliance teams to enforce data protection, retention, and access policies
- Provide documentation, architecture diagrams, and operational standards for the data platform and pipelines
Skills, Knowledge and Expertise- 5 or more years of experience in data engineering, data integration, or data platform development
- Strong hands-on experience with Azure Data Factory, Azure Synapse, Microsoft Fabric, and related Azure data services
- Proficiency in SQL, DAX, Power Query, and data modeling for Power BI
- Experience designing and maintaining Power BI semantic models, datasets, and row-level security configurations
- Familiarity with data governance, cataloging, and lineage management in tools like Microsoft Purview
- Experience building and optimizing cloud data pipelines with structured, semi-structured, and unstructured data
- Understanding of data preparation for AI and machine learning applications, including RAG architectures
- Exposure to engineering and geospatial data such as CAD, BIM, and GIS
- Strong analytical and problem-solving skills with a focus on scalability and performance
- Excellent collaboration and communication skills across technical and business audiences
- Bachelor's degree in Computer Science, Data Engineering, or related field preferred
- Microsoft certifications such as Azure Data Engineer Associate or Fabric Analytics Engineer Associate are a plus
- May require occasional travel to Trilon offices or partner locations for integration or collaboration activities
Pay TransparencyThe base salary range for this role is indicated in the posting. This range reflects the company's good faith estimate of the compensation for this position at the time of posting. Final compensation will be determined based on factors such as experience, skills, qualifications, internal equity, and geographic location.
