AI‑Powered Systems Integrator: Data Tools for Fund Management

Octopus Energy

Greater London

On-site

GBP 80,000 - 110,000

Full time

6 days ago

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Job summary

Octopus Energy OEGEN is hiring for a data/integration role to connect third-party systems and internal apps to our Databricks environment, building data products and AI-enabled workflows for fund management and finance teams.

You will design and ship internal tools using Python, Streamlit, FastAPI and related tech, while reusing existing capability from OE Group's data platform. The role blends integration and product development in a small team.

Qualifications

Strong systems integration experience via REST APIs, connectors and authentication flows.
Proven track record of building data products or internal applications used by the business.
Solid Python and SQL, used daily for building, automation and data modelling.
Hands-on experience with a cloud data platform in production (Databricks preferred).
Practical experience integrating AI into real tools with LLM APIs, retrieval, agents.
Practical identity and access experience - SSO and credential management.
Experience with Microsoft 365 / SharePoint integration.
Experience keeping cloud or AI usage costs under control.
Pragmatic approach to adopting existing infrastructure over building new.
Clear communication to explain trade-offs to a fund manager and technical reviews.
Ownership mindset in a small team.

Responsibilities

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.