Senior Data Platform Engineer

About This Opportunity

We are representing a confidential renewable energy investment firm in their search for a Senior Data Platform Engineer. Our client manages a global portfolio of utility-scale renewable assets and is building production-grade infrastructure to enable AI-powered analytics and decision-making across their investment operations.

This role sits within the Systems and Data team and focuses on hardening platform architecture, implementing enterprise-grade governance controls, and enabling safe deployment of AI tools across Finance, Asset Management, and Fund Operations.

Location: London, UK

Responsibilities

You will turn existing platform architectures and prototype patterns into production-ready infrastructure, working alongside data engineers, application builders, and business stakeholders.

Platform Engineering & CI/CD:

• Build and maintain Infrastructure-as-Code using Terraform across development, test, and production environments
• Strengthen CI/CD practices in GitHub Actions with repeatable deployment patterns, environment promotions, automated rollbacks, and release controls
• Convert architectural designs into maintainable, well-documented engineering patterns

Identity, Access & Security:

• Design and implement federated identity architecture across Okta, Entra ID, and ZScaler
• Build identity, token exchange, and permission propagation models for both human users and non-human AI agents
• Implement SCIM provisioning/deprovisioning with single-revoke capability and full auditability

AI Gateway & Governance:

• Harden AI gateway and agent orchestration layers into secure, production-ready capabilities
• Implement API gateway controls including authentication, routing, rate limiting, token budgets, policy-as-code, and action logging
• Build platform-, tool-, and agent-level kill switches with dry-run and safe-testing modes

Observability & FinOps:

• Implement AI system observability including prompt logging, output monitoring, quality scoring, and drift detection
• Establish operational monitoring for gateway usage, latency, error rates, and model routing logic
• Enforce token budgets, cost alerts, and usage/cost attribution by team and use case

Enablement & Integration:

• Deploy vector database and retrieval infrastructure to support Document AI and RAG use cases
• Align platform controls with Databricks and Unity Catalog governance patterns
• Provide documentation and reusable patterns for self-service onboarding

What We're Looking For

Essential skills and experience:

• Proven experience designing and implementing enterprise-grade federated identity, token exchange, permission propagation, SCIM provisioning, and access lifecycle management
• Strong hands-on experience with Infrastructure-as-Code (Terraform) and production CI/CD pipelines including environment promotion, rollback, and release controls
• Experience hardening API gateway patterns with authentication, routing, rate limiting, and access policy enforcement
• Technical comfort working across Azure, AWS, or Databricks platform ecosystems
• Highly autonomous engineer skilled at translating architectural specs into practical code and communicating trade-offs clearly

Highly desirable:

• Experience with AI gateways, LLM proxies, or model-routing patterns
• Exposure to AI observability tools, vector databases, RAG pipelines, or non-deterministic system monitoring
• Hands-on experience implementing policy-as-code, action logging, or AI kill switches
• Direct experience with Okta, ZScaler, Entra ID, Unity Catalog, or M365 migration environments
• Background in regulated or high-governance industries such as Financial Services, Energy, Infrastructure Investment, Fintech, or Enterprise SaaS
• Familiarity with FinOps practices including token budgeting, usage tracking, and cost attribution

Technology ecosystem: Databricks, Unity Catalog, Entra ID / Azure Identity, Okta, ZScaler, GitHub Actions, MCP patterns, Terraform

How We Work With You

When you apply through Get A Job.ai, our talent team will review your background and arrange an initial screening conversation. We'll discuss your experience with federated identity systems, platform engineering, and AI governance patterns to ensure strong alignment with the role requirements.

Once we confirm you're a strong match, we'll submit your profile to our client for consideration. The client's process typically takes up to 4 weeks and includes conversations with their recruitment team and technical leadership.

Please do not contact the client directly—all communication and interview coordination will be managed through our recruitment team to ensure the best experience for you.

Pay

Compensation details will be discussed during your initial conversation with our recruiters, as our client offers flexibility based on experience level. All offers and payments are processed through Get A Job.ai.

Equal Opportunity: Get A Job.ai is committed to fostering an inclusive recruitment process. We evaluate all candidates based on skills, experience, and potential, and we do not discriminate on the basis of any protected characteristic. We encourage applications from all qualified individuals, regardless of background.