Data Engineer

Trilon; United States; On-site; USD 116,000 - 155,000; Full time

Job summary

Trilon is seeking a Data Engineer to own and maintain our enterprise data platform, integrating Azure Data Factory, Synapse, and Microsoft Fabric. You will build scalable data pipelines, secure Power BI models, and ensure governance across acquisitions.

You will collaborate with analysts, AI teams, and cybersecurity to deliver trusted data for BI and AI initiatives. This remote role reports to the IT department and offers a competitive salary.

Qualifications

• 7+ years of experience in data engineering or data platform development.
• Strong experience with Azure data services and Power BI.
• Experience designing scalable data models and governance.
• Bachelor’s degree in CS or related field preferred.
• Microsoft certifications are a plus.

Responsibilities

• Own and steward the enterprise data platform.
• Design, develop, and maintain data pipelines and transformations.
• Build secure, governed Power BI semantic models for analytics.
• Collaborate with analysts and stakeholders on reporting needs.
• Partner with cybersecurity and compliance for data governance.

Skills

SQL

DAX

Power Query

Data Modeling

Data Governance

Collaboration

Education

Bachelor’s degree in Computer Science or related field

Tools

Azure Data Factory

Azure Synapse

Microsoft Fabric

Power BI

Job description

Compensation: $116,000 - $155,000 / year

Department: IT

Location: Remote- USA

Compensation: $116,000 - $155,000 / year

Description

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 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

• 7 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

About Trilon

Trilon 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.trilon.com.

Pay Transparency

The 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.