Apps Dev Tech Lead Python Data Engineer - C13 - RUTHERFORD

Build systems that shape credit decisions—at scale.

Join a fast‑moving Credit Risk Technology team where modern engineering, data, and AI come together to drive real‑world financial impact. We operate with a startup mindset—ownership, speed, and experimentation—within the rigor and responsibility of a global financial institution. You’ll help design and deliver mission‑critical platforms that support credit risk assessment, analytics, and decisioning across the firm.

The Apps Dev Tech Lead Python Data Engineer is a senior engineering leadership role focused on building and evolving modern, scalable platforms for credit risk management. You will lead application design and development while partnering closely with risk, data, and platform teams to deliver systems that are performant, resilient, and compliant.

This role blends hands‑on development, system architecture, technical leadership, and AI‑enabled innovation, with a strong emphasis on microservices, cloud‑native design, and enterprise‑grade governance.

• Architect, design, and deliver scalable, Python‑based applications supporting credit risk analytics, workflows, and reporting.
• Partner with risk, product, and technology leadership to integrate platforms, identify enhancements, and enable new products and process improvements.
• Resolve high‑impact, complex initiatives through deep analysis of business processes, system flows, and industry standards.
• Ensure solutions adhere to enterprise architecture, data, security, and infrastructure blueprints.
• Establish and enforce engineering standards for coding, testing, CI/CD, debugging, and production readiness.
• Design and evolve microservices‑based architectures, ensuring scalability, resiliency, observability, and maintainability.
• Apply AI and GenAI capabilities to modernize workflows, automate analysis, and unlock new insights in credit risk.
• Serve as technical leader and mentor, coaching mid‑level engineers and analysts and allocating work as needed.
• Apply sound risk and control judgment, ensuring compliance with laws, regulations, and policies while safeguarding clients, data, and the firm.

• Operate with a startup mindset: ownership, bias for action, and pragmatic innovation.
• Deliver with enterprise discipline: stability, controls, transparency, and audit readiness.
• Balance experimentation with responsibility in a high‑trust, high‑impact environment.
• Adapt quickly as priorities change while maintaining long‑term system integrity.

• 10–15 years of experience in application development or systems engineering within complex environments.
• Advanced proficiency in Python and SQL, with strong software engineering fundamentals.
• Hands‑on experience building API‑driven services using FastAPI, Pydantic, and/or Django.
• Proven expertise designing and implementing microservices architectures, including service decomposition, inter‑service communication, resiliency patterns, and observability.
• Strong experience with Docker and Kubernetes, deploying and operating containerized services in production.
• Deep understanding of system architecture, data flows, and distributed systems.
• Experience working in Linux environments, including shell scripting and operational troubleshooting.
• Strong track record implementing unit testing, TDD, and automated quality controls.
• Subject Matter Expert (SME) in at least one application, platform, or service domain.

• Working knowledge of large language models (LLMs) and modern AI platforms from leading providers such as OpenAI, Anthropic, Google, and Meta.
• Experience designing or contributing to LLM‑enabled solutions (e.g., copilots, workflow automation, analytics augmentation).
• Familiarity with prompt engineering, model integration patterns, and AI governance considerations in enterprise environments.
• Exposure to modern “vibe coding” practices—leveraging AI‑assisted tooling to accelerate development, experimentation, and problem solving while maintaining engineering rigor.

• Experience with distributed data and compute platforms (e.g., Spark, PySpark, Hadoop, Hive).
• Hands‑on experience with graph databases, particularly Neo4j, for network, relationship, or dependency‑driven use cases.
• Background in credit risk, financial risk management, or banking platforms.
• Experience modernizing or decomposing legacy monolithic systems in large enterprises.
• Proven delivery of GenAI / AI‑driven solutions in regulated or large‑scale environments.

• Bachelor’s degree (or equivalent experience) in Computer Science, Engineering, Mathematics, or a related STEM field.
• Master’s degree preferred.