Python Technical Delivery Lead — Cloud Data Platforms (AWS/Databricks/Terraform)
On-siteLondon, England, United Kingdom
Job Summary
Provide technical guidance to business and technical teams while developing secure, high-quality production code and reviewing others' work. Establish an enterprise-aligned operating model for AI-assisted engineering, defining validation standards for code quality, delivery speed, and operational outcomes. Set the toolchain strategy across planning, source control, build, test, security, deployment, and observability to drive standardization. Act as a function-wide subject matter expert in Python, cloud native technologies, and responsible AI governance for engineering workflows. Contribute to the engineering community as an advocate for firmwide frameworks and practices.
Required Qualifications
- Formal training or certification on software engineering concepts
- Advanced applied experience in Python
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s)
- Ability to tackle design and functionality problems independently with little to no oversight
- Practical cloud native experience
Desired Qualifications
- Establishes a scalable operating model for enterprise-authorized AI-assisted development (approved use cases, guardrails, roles/approvals, auditability) and drives consistent adoption across multiple teams.
- Sets clear validation expectations for AI outputs before merge/release—mandatory peer review, secure coding checks, automated test coverage thresholds, and documented acceptance criteria for high-risk or customer-impacting changes.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
- Establishes and enforces responsible AI governance for engineering workflows (approved use cases, data classification rules, escalation paths, auditability), ensuring adoption aligns with enterprise policy and regulatory expectations.
- Defines secure handling standards for AI inputs/outputs—prohibiting sensitive data exposure, mandating redaction/minimization, controlling access via least privilege, and ensuring appropriate logging and retention practices.
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