Systems Integrator

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.

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.

Requirements

• 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.

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