Systems Integrator

Help us make a big green dent in the universe. We're on a mission to drive the global renewable energy revolution, and we need many more people to help us make our vision a reality. 

Octopus Energy Generation (OEGEN) is a leader in sourcing, investing in, and managing a wide range of utility-scale renewable energy assets. As we continue to expand our investment base, we are dedicated to building a diverse team that reflects the communities we serve. Currently, we manage a global portfolio of over £8 billion in renewable energy assets. 

We are committed to delivering best-in-class responsible investment activity, offering like-minded investors the means and products to invest their capital sustainably. It is our ambition to change the entire energy lifecycle and make every green electron matter, delivering the best outcomes for customers, investors, the environment and society, accelerating the transition to net zero. 

OEGEN Data, Tech & Transformation Team 

We are embarking on a comprehensive transformation program designed to drive significant advancements in our business operations and technology landscape. Our primary goal is to transition from our current reliance on legacy systems and specialized off-the-shelf tools to a more integrated, AI-driven, and data-centric operational model. 

Leveraging insights and guidance from our other technically mature entities like Kraken and OE, we plan to adopt cutting-edge tools and capabilities, particularly in AI and Python-driven solutions. 

By investing in a diverse range of skills, we aim to create a hybrid team capable of operating in both our legacy and future environments, ultimately transforming OEGEN into an efficient, data-driven organization. 

About the role 

The tools are already here. Like the rest of OE Group, OEGEN runs on Databricks, we're moving onto our own Microsoft 365 tenant, and Octopus Energy Group gives us access to a powerful set of shared data and AI capabilities. What we're missing is someone whose job is to make all of it work together for our Fund Management business - and then build genuinely useful things on top of it. 

This is a two-sided role. On one side, you'll integrate: wiring third-party systems, internal apps and document storage into the environment we already have, setting sensible guardrails around access and cost, and adopting existing Group capability wherever it does the job rather than rebuilding it. On the other, you'll build: turning those connections into data products, internal tools and AI-enabled workflows that fund management, asset management and finance use every day. Tying the two together, you'll develop and own the playbook - the documented, repeatable patterns for how a new system gets connected, how a tool gets built and shipped, and how both get supported afterwards - so that what you learn once becomes how the whole team works. The two halves feed each other: you'll understand what's possible because you built the plumbing, and you'll build better plumbing because you know what people actually do with it. 

We want someone with genuine ideas about where AI can take a business like ours, and the pragmatism to ship them. Our team has already built an Excel data mart plugin that puts gold-layer tables directly in the hands of non-technical staff, a metrics taxonomy app that keeps third-party definitions aligned with our own, and AI-assisted workflows that scaffold documentation automatically. There's a long list of things we haven't got to yet. Expect roughly half your time on integration and enablement and half on building, and expect to work closely with the central data platform team so what we do in OEGEN stays consistent with the wider estate and our specific needs get onto their roadmap. 

• 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. 

• 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. 

 

• 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.