Data Engineer, Global Credit Technology

Summary The Data Engineer, Credit Data & Applications is a hands-on engineering role within Carlyle's Global Credit Technology team. This individual will contribute to the ongoing development and enhancement of the Credit Data Warehouse (CDW), which supports portfolio analytics, loan performance monitoring, trade tracking, fund reporting, and external integrations. This role is primarily focused on data engineering, transformation frameworks, orchestration, and system integrations. While the team builds applications on top of CDW, dedicated application engineering resources lead full-stack UI development. This position will focus on building and maintaining reliable data pipelines, implementing business logic, and supporting scalable data solutions that power those applications. The ideal candidate has strong hands-on experience with modern cloud data platforms (Snowflake and/or Databricks), transformation tooling (dbt), and orchestration frameworks (Airflow or similar). This individual works effectively in a business-aligned environment, partnering with investment and operations teams to deliver secure and scalable data solutions.

Credit Data Warehouse Architecture & Development (40%) Build and enhance scalable data models within Snowflake (and/or Databricks where applicable) across landing, integration, and presentation layers, following established architectural patterns. Develop and maintain transformation logic using dbt, ensuring modular, testable, and well-documented models Optimize SQL performance and warehouse resource usage for large-scale financial datasets Implement data quality checks, validation rules, and audit controls Contribute to metadata-driven approaches that support flexible integrations and reporting needs. Support lineage, governance, and maintainability of CDW assets through documentation and adherence to engineering standards. Design data models optimized for reporting and BI consumption, partnering closely with the Credit IQ (Power BI) team to ensure scalable semantic layers and performant analytics Workflow Orchestration & Pipeline Engineering (25%) Build and support data pipelines orchestrated through Airflow Develop and maintain DAGs/workflows for ingestion, transformation, external extracts, and API integrations. Support improvements to pipeline reliability, monitoring, and error handling in production environments. Collaborate with DevOps to ensure CI/CD alignment and production stability Support modernization of legacy orchestration processes into Airflow-based frameworks Integrations & Data Products (20%) Build and support integrations with external vendors, fund administrators, trustees, and internal systems. Build scalable export frameworks (SFTP, API, file-based extracts) driven by configuration and metadata Support data consumption by internal applications and analytics tools Collaborate with application engineering teams to provide well-structured datasets and data interfaces for application use. Applied AI & Intelligent Data Use Cases (10%) Support AI-enabled workflows by preparing structured and unstructured data for retrieval and analysis use cases Assist in enabling retrieval-based workflows leveraging CDW datasets where applicable Ensure AI-related datasets follow security and governance standards Cross-Functional Collaboration & Technical Leadership (5%) Participate in code reviews and provide guidance to junior and offshore contributors Coordinate assigned work with offshore resources to ensure clarity of requirements and timely delivery Work with investment and operations teams to translate business workflows into scalable data solutions Promote clear documentation and adherence to engineering best practices Requirements Education & Certificates Bachelor's degree, required Concentration in computer science, engineering, or a related quantitative field, preferred Master's degree preferred Professional Experience Minimum of 6 years of overall