Tradeweb Markets is looking to modernize its Finance systems by implementing and customizing a complex SaaS billing platform, integrating Finance systems into a centralized data lake, and scaling Finance AI use cases.
Requirements
- 2–3 years of experience coding in Python, including real-world or production use
- Skilled at writing and debugging complex SQL queries
- Hands-on experience with ETL or stream processing tools such as Kafka, Spark, Flink, Airflow, or Prefect
- Familiarity with modern AI/ML frameworks (e.g. Scikit-learn, PyTorch, TensorFlow, Hugging Face, MLflow, Kubeflow)
- Experience building, fine-tuning, or integrating machine learning or generative AI solutions
- Comfortable using AI-assisted development tools like GitHub Copilot, ChatGPT, or Code Interpreter
Responsibilities
- Design data models and schemas to support billing, revenue, and financial reporting processes
- Build core engineering components, including ETL pipelines, mediation logic, and rating algorithms for revenue calculation and invoice generation
- Implement and customize a third-party SaaS billing platform tailored to enterprise Finance needs
- Drive data integration initiatives to connect Finance systems with the firmwide data lake
- Lead development of Finance-focused AI use cases, including prototyping, testing, and scaling applications across workflows
- Troubleshoot production issues and own platform support across billing, reporting, and data pipelines
- Build connectors to move trade and financial data between trade repositories, billing systems, and financial platforms
Other
- Partner with Finance, Product, Technology, and Data teams to gather requirements and translate them into scalable solutions
- Contribute to code reviews, QA tracking, and continuous improvement of engineering practices
- Write rigorous unit tests and uphold high standards for reliability and accuracy in financial data processing
- Proactive, curious, and driven — a go-getter who’s excited to take ownership and push projects forward
- Enjoys solving messy, complex problems by breaking them into simple, elegant pieces
