Senior Data Engineer (Etl)

This role is primarily focused on data engineering, transformation frameworks, orchestration, and system integrations. While the team builds applications on top of their platform, 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 Integrations & Data Products (20%) 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 Ensure AI-related datasets follow security and governance standards 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 Bachelor's degree, required Concentration in computer science, engineering, or a related quantitative field, preferred Experience in data engineering, platform engineering, or backend-focused software engineering roles, required Strong hands-on experience designing and operating solutions in Snowflake and/or Databricks (or comparable modern cloud data warehouse/lakehouse platforms) Experience building and deploying data platforms in Azure and/or AWS cloud environments Strong experience writing and optimizing SQL for analytical and financial datasets Hands-on experience with dbt for transformation management, testing, and modular model development Experience developing and supporting Airflow-based orchestration pipelines (or equivalent), including workflow design and monitoring Experience building and maintaining enterprise-grade data pipelines across ingestion, transformation, and presentation layers Exposure to financial services, alternative asset management, or credit products strongly preferred Experience collaborating with offshore or distributed engineering teams preferred Experience supporting reporting and BI environments (e.g., Power BI), including building reporting-friendly data models and optimizing warehouse performance for analytics workloads Python experience for pipeline tooling, automation, and integration services Azure Data Factory, Azure Storage, Azure AD) and/or AWS services (e.g., Experience building or supporting data services consumed by applications Exposure to applied AI or LLM-enabled workflows in a production setting Familiarity with Power BI or comparable BI/reporting tools and semantic modeling concepts Strong SQL and analytical data modeling skills Snowflake and/or Databricks fundamentals, including performance and cost considerations dbt framework design and transformation best practices Azure and/or AWS cloud-native data architecture API integration and service-oriented data patterns Data quality, lineage, governance, and audit controls Ability to translate business workflows into scalable data solutions Understanding of BI/reporting architecture, semantic modeling, and analytics performance