Data Engineer Department: IT Job Description: Employment Type: Full Time 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 - 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 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.

Role Description We are looking for a talented and experienced Senior Data Engineer I to join our growing data ingestion team. In this role, you will collaborate with data scientists, analysts, marketing, sales, and application developers to design, develop, and maintain scalable data systems and pipelines that power critical business insights. This is an exciting opportunity to work in a fast-paced environment and have a significant impact on our data infrastructure. Essential Duties: - Solve Complex Problems: Apply your broad experience and expertise to develop innovative solutions for complex data challenges. Use professional judgment to determine the best methods and techniques for problem-solving, even in unfamiliar territory. - Data Architecture & Engineering: Design, develop, and maintain high-performance, scalable, and testable data pipelines and ETL processes. - Cross-Functional Collaboration: Partner with data science, analytics, product, marketing, and engineering teams to translate business needs into scalable data solutions. Network with senior internal and external stakeholders to enhance data strategies. - Autonomy & Ownership: Work with minimal oversight, ensuring technical integrity in all solutions. Define methods and procedures for new assignments and contribute to the coordination of data engineering activities. - Optimize & Maintain Data Infrastructure: Ensure system performance, reliability, and security by continuously improving existing data platforms. - Data Governance & Quality: Implement and advocate for best practices in data governance, security, and quality assurance. - Leadership & Mentorship: Influence project planning and timelines, provide actionable feedback, and mentor junior engineers to foster a high-performing, collaborative team. - Stay Ahead of Industry Trends: Keep up with emerging technologies and best practices to continuously improve data engineering processes. Qualifications - Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent experience). - 5+ years of experience in data engineering, with a proven track record of solving complex problems and scaling data pipelines. - Strong SQL skills and experience with data processing frameworks (e.g., Apache Spark, Apache Kafka). - Expertise in cloud data platforms (AWS). - Proficiency in Python, Java, or Scala. - Experience with AWS Glue. - Solid understanding of data warehousing concepts (e.g., Redshift). - Strong analytical and problem-solving skills, with the ability to work independently and exercise good judgment. Requirements - Hands-on experience with ETL tools like Airflow. - Experience with DBT (data build tool). - Experience with data modeling, governance, and quality assurance. - Experience in software application development. - Experience with containerization and orchestration tools (Docker, Kubernetes). - Familiarity with machine learning workflows and model deployment pipelines. - Power BI management and report building. - Knowledge of data security best practices and frameworks. Benefits - Comprehensive benefits package. - Flexible work options for work/life balance. - Volunteer time off. Company Description Headquartered in Kansas City, USA, C2FO has more than 400 employees worldwide, with operations throughout North America, Europe, India, Asia Pacific, and Australia. C2FO is the world’s largest on-demand working capital platform. Our mission is to ensure every business has the capital needed to thrive and we have delivered more than $500+ billion in funding to businesses since our founding.

• We’re looking for a Staff Data Engineer to join a multi-disciplinary engineering team building modern, enterprise-grade data platforms. • In this role, you will design scalable data lakes, warehouses, and pipelines, define governance and quality standards, and drive data platform modernization across real, in-flight work where performance, reliability, and security are critical. • You’ll mentor more junior engineers, partner with leadership on data strategy, and bring an AI-forward mindset. • Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes. • Build and optimize scalable data pipelines supporting batch and real-time processing. • Define and enforce data governance, quality standards, and compliance frameworks across the platform. • Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation. • Drive data platform modernization, optimizing for performance, cost, and scalability. • Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams. • Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows. • Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption.

Role Description We’re seeking a Mid-Level Data Engineer/Analyst to independently design, build, and optimize data pipelines and analytics solutions that power business intelligence and AI/ML initiatives. In this role, you will own key data workstreams end to end, build production-grade transformation layers using dbt and Spark, manage data infrastructure on Snowflake and Databricks, and collaborate with analysts, data scientists, and product teams to deliver reliable, well-governed, and high-quality data products. You will also contribute to the maturity of our DataOps and data observability practices. - Design, build, and maintain production-grade ETL/ELT pipelines using dbt, Apache Spark (PySpark), Airflow, Dagster, or Prefect. - Develop and optimize data models on Snowflake, Databricks, BigQuery, or Redshift following dimensional modeling, data vault, or One Big Table patterns. - Implement and manage data ingestion from diverse sources including databases, REST/GraphQL APIs, event streams (Kafka, Kinesis), SaaS platforms, and flat files using Fivetran, Airbyte, or custom connectors. - Build and maintain semantic/metrics layers and curated data products for analytics, reporting, and self-service consumption. - Implement data quality, testing, and observability frameworks using dbt tests, Great Expectations, Soda, Monte Carlo, or Datafold. - Create advanced dashboards, reports, and analytical visualizations using Tableau, Looker, Power BI, or Sigma Computing. - Optimize query performance, pipeline efficiency, and cloud data platform costs across Snowflake, Databricks, or BigQuery. - Collaborate with data scientists and ML engineers to prepare and serve feature datasets for machine learning models. - Implement DataOps practices including CI/CD for data pipelines, version-controlled transformations, and automated testing. - Write production-quality Python and SQL code with proper testing, documentation, and error handling. - Support data governance initiatives including cataloging, lineage tracking, access controls, and PII management using tools like Alation, Atlan, DataHub, or Unity Catalog. Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. - 3–5 years of professional experience in data engineering, analytics engineering, or a closely related role with production delivery. - Strong proficiency in SQL and experience writing complex transformations, window functions, CTEs, and performance-tuned queries. - Hands-on experience with at least one modern data platform: Snowflake (strongly preferred), Databricks, BigQuery, or Redshift. - Experience with dbt (data build tool) for data transformation, testing, and documentation in production environments. - Working knowledge of Python (Pandas, PySpark, or Polars) for data processing and pipeline development. - Experience with workflow orchestration tools: Airflow, Dagster, Prefect, or cloud-native equivalents (AWS Step Functions, Azure Data Factory). - Familiarity with data ingestion tools and patterns: Fivetran, Airbyte, CDC (Debezium), or streaming ingestion (Kafka, Kinesis). - Experience with data visualization and BI tools: Tableau, Looker, Power BI, or Sigma. - Understanding of data modeling methodologies (Kimball, Data Vault, OBT) and data warehousing best practices. - Familiarity with version control (Git), CI/CD for data, and Agile development workflows. Preferred Qualifications - Snowflake SnowPro Core, Databricks Data Engineer Associate, or AWS Data Analytics Specialty certification. - Experience with Apache Spark and Databricks for large-scale data processing and lakehouse architectures. - Familiarity with data cataloging and governance tools: Alation, Atlan, DataHub, Collibra, or Databricks Unity Catalog. - Experience with data observability platforms: Monte Carlo, Datafold, Soda, or Elementary. - Exposure to streaming data pipelines using Kafka, Spark Structured Streaming, Flink, or Kinesis. - Experience with metrics/semantic layers: dbt Semantic Layer, Cube, or Looker Modeling Language (LookML). - Knowledge of cloud data infrastructure: AWS (S3, Glue, Athena, Redshift, Lake Formation), Azure (ADLS, Synapse, Data Factory), or GCP (GCS, Dataflow, BigQuery). Benefits - Unlimited PTO. - Very generous parental leave, much above industry standards. - Entrepreneurial culture where pushing limits and taking risks is everyday business. - Open communication with management and company leadership. - Small, dynamic teams = massive impact. - Medical, Dental and Vision coverage for employees. - Access to Disability & Life insurance. - Mental health and wellbeing support. - Annual bonus program. - Employer Stock Purchase Program (ESPP). - Yearly Team building experiences. - Mentorship and sponsorship opportunities. - Manager resources and support.