Senior Data Engineer (Python) - Remote

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