Systems Integrator

NextGenEnergyJobs; Greater London; Hybrid; GBP 65,000 - 95,000; Full time

Job summary

Octopus Energy Group is seeking a data integration specialist to unify business systems with our Databricks environment, building reusable datasets for business consumption through Excel, dashboards, and natural-language queries. You will design AI-enabled tools and internal apps for fund and finance teams, and work with cross-functional data platform squads to reuse existing capabilities.

We seek practical, ownership-minded engineers who can ship pragmatic solutions, implement governance, and

Qualifications

• Strong systems integration experience across SaaS tools, data sources and REST APIs.
• Track record of building and shipping data products or internal applications used by business.
• Solid Python and SQL used daily for automation and data modelling.
• Hands-on production experience with a cloud data platform (Databricks preferred).
• Experience integrating AI into real tools (LLM APIs, retrieval, agents).
• Identity and access management experience (SSO, service accounts, credential management).
• Experience with Microsoft 365 / SharePoint integration.
• Proven ability to manage cloud/AI costs and attribute spend.
• Pragmatic approach: configure existing infrastructure before building new; ship when possible.
• Clear communicator able to explain trade-offs to non-technical stakeholders and in technical reviews.
• Comfort with ownership in a small team to set working patterns.

Responsibilities

• Integrate business systems into the Databricks environment via APIs and connectors.
• Build data products on top of connected data for Excel, dashboards, and NLQ.
• Design and ship internal apps and AI-enabled tools for fund/asset/finance teams.
• Prototype and productionise new AI capabilities from document AI to agentic workflows.
• Reuse existing platform capabilities and adapt them for OEGEN.
• Set up guardrails in Databricks and Unity Catalog for secure, scalable use.
• Drive cost awareness with tagging, budgets, and usage monitoring.
• Credential and log AI usage with managed keys and patterns for embedding AI.
• Standardise internal app development with templates, authentication, deployment, and docs.
• Consolidate ad-hoc scripts and no-code work into fewer, well-understood integrations.
• Collaborate with non-technical teams to translate problems into builds.

Skills

Systems integration

Data product development

Python

SQL

Databricks

AI integration

Identity and access management

Microsoft 365 / SharePoint

Cost management

Technical communication

Ownership in small teams

Tools

Databricks

Unity Catalog

Lakebase

FastAPI

Streamlit

HTMX

Microsoft 365

SharePoint

REST APIs

Job description

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.