Senior Data Systems Lead

What You'll Do

Integrate the systems our business runs on - third-party providers, internal tools, our own SharePoint and Microsoft 365 tenant - into our existing Databricks environment, via APIs and existing connectors.

Build data products on top of what you connect: well-modelled, documented, reusable datasets that the business consumes directly through Excel, dashboards and natural-language querying, rather than one-off extracts.

Design and ship internal applications and AI-enabled tools - using Python, Streamlit, Databricks Apps (HTMX), Lakebase, FastAPI or similar - that put data and AI directly into the hands of fund management, asset management and finance teams.

Prototype, evaluate and productionise new AI capability, from document AI and retrieval to agentic workflows, and turn the ideas that prove out into supported products rather than abandoned experiments.

Take an adopt-first approach: work with Octopus Energy Group's data platform and AI teams to reuse capability that already exists, and configure it for OEGEN rather than building parallel versions of it.

Set up practical guardrails inside Databricks and Unity Catalog - schema and workspace access, permissions, environment hygiene - so more teams can use it without stepping on each other.

Bring cost management from reactive to proactive: tagging, budgets, alerting and usage monitoring, so Databricks and AI token spend can be attributed to the right team, project or fund before the invoice arrives.

Get our AI usage properly credentialed and logged - managed keys and access rather than API keys sitting in individual password managers - and provide clean patterns for embedding AI into the tools we build.

Standardise how we build and ship internal apps: shared templates, authentication, deployment and documentation, so a tool remains supportable by someone other than the person who wrote it.

Consolidate the ad-hoc scripts, spreadsheets and no-code workflows we've accumulated into fewer, better-understood integrations that don't need babysitting, and automate the repetitive parts of our own workflow.

Work directly with non-technical teams to understand their problems and translate them into things we can build.

What You'll Need

Strong systems integration experience - connecting business systems, SaaS tools and data sources via REST APIs, connectors and authentication flows.

A track record of building and shipping data products or internal applications that people actually use, not just pipelines that feed someone else's reports.

Solid Python and SQL, used daily for building, automation and data modelling.

Hands-on experience with a cloud data platform in production (Databricks preferred): workspaces, catalogs, permissions and compute.

Practical experience integrating AI into real tools - LLM APIs, retrieval, agents - with a clear-eyed sense of what works and what's still a demo.

Practical identity and access experience - SSO, service accounts, credential management and access lifecycle - enough to make sensible, secure choices without a security team holding your hand.

Experience with Microsoft 365 / SharePoint integration, or a comparable enterprise document and collaboration stack.

Real experience keeping cloud or AI usage costs under control: you know what drives the bill and how to attribute and cap it.

A pragmatist's instinct for adopting over building where infrastructure is concerned - you'd rather configure something that exists than write something new - paired with a bias toward shipping when it comes to solutions.

Clear communication. You can explain a trade-off to a fund manager and hold your own in a technical review with a central engineering team.

Comfort with real ownership in a small team, where you set the working patterns rather than inherit them.

Bonus Points For

 

Full-stack or application development experience, particularly with Streamlit, FastAPI or HTMX.

Familiarity with the Databricks ecosystem - Unity Catalog, Databricks Apps, Lakebase, Genie - or equivalent tooling.

Experience with vector search, embeddings, document AI or retrieval-augmented generation in production.

Experience with Terraform, GitHub Actions or similar, to keep configuration version-controlled and repeatable.

FinOps experience: usage monitoring, budgets, showback and chargeback.

Having supported analysts and non-specialist builders using a shared data environment.

Experience in a regulated or audited setting such as financial services, infrastructure investment or energy.

Familiarity with renewable energy, infrastructure investment or financial services data.