AI and Analytics Engineer

Summary

We are looking for a pragmatic and versatile AI & Analytics Engineer to help design and deliver scalable analytics solutions across our modern data platform.

This role sits across analytics engineering, solution architecture, and lightweight application development. You will work across Microsoft Fabric, Azure, Python, SQL, and React to turn data and insights into usable data solutions that deliver proven value to the business.

You will help stitch together outputs from across the wider data function - including data engineering, advanced analytics, and AI initiatives - into robust, maintainable, and user-focused solutions that can scale as requirements evolve.

This is not a narrowly defined "ticket delivery" role. We are looking for someone comfortable operating across the full stack, contributing to architecture discussions, and helping shape how modern analytics capabilities are delivered across the business.

Responsibilities

• Design, build, and maintain data-driven solutions that help automate or optimise existing processes, or deliver brand new capabilities.
• Contribute to the wider Azure estate - including Azure Functions, Container Apps, Key Vault, and related services - following agreed engineering patterns and standards.
• Support the development of AI-powered tooling, including natural language query agents, semantic retrieval, and RAG pipelines over internal data assets.
• Participate in the architecture and design of scalable data solutions, such as MDM frameworks, semantic layers, reusable analytics capabilities, and cross-platform integration patterns.
• Collaborate on frontend development activities in React/TypeScript where analytics solutions require a user interface or operational tooling.
• Help bridge the gap between technical capability and business need by contributing to solution design discussions, engineering standards, reusable frameworks and shaping practical delivery approaches.
• Document what you build as a first-class part of delivery - including semantic metadata, lineage, architecture decisions, assumptions, and operational considerations.

Skills & Attributes

Technical skills

Essential:

• Microsoft Fabric
• Python
• Advanced T-SQL
• Data modelling
• REST APIs
• Git/DevOps workflows
• CI/CD concepts and practices
• Azure (general platform knowledge and hands-on experience)
• React/TypeScript (working knowledge and willingness to contribute)
• Experience designing scalable data or analytics solutions

Desirable:

• React/TypeScript (strong hands-on experience with proven track record of delivery)
• Deploying Azure Container Apps and other Azure resources
• LLM/RAG implementation patterns
• Power BI, Semantic models + DAX
• DBT
• FastAPI, DuckDB, Polars
• Kimble methodologies
• YAML pipelines
• Microsoft Graph API
• Machine Learning (high-level conceptual understanding)
• Experience building internal operational or analytics tooling

Soft skills / ways of working

• Proactive and pragmatic: you take ownership, prioritise well, and focus on delivering outcomes - not just activity.
• Investigative mindset: you don't just rely on tried and tested methods - you are mindful of emerging tech, libraries and approaches as the wider data landscape evolves.
• Strong written and verbal communication: able to explain complex analysis and data concepts clearly to both technical and non-technical audiences, adapting style to the stakeholder.
• Confident engaging with stakeholders: comfortable gathering requirements, attempting to reduce ambiguity, translating needs into automated data solutions and are not afraid to challenge/push back if the requested outcomes don't meet our strategic principles; for example, if the proposed solution would struggle to scale, or perhaps would introduce significant ongoing manual overhead.

This role will suit you if:

• You are a pragmatic engineer - comfortable shipping working solutions, learning quickly, and iterating over time rather than over-engineering upfront.
• You are comfortable with ambiguity - requirements will not always be fully defined, and you are able to work backwards from desired outcomes and ask the right questions.
• You can operate across the data spectrum - happy moving between SQL, Python, infrastructure, architecture discussions, and frontend work without needing rigid boundaries between disciplines.
• You have a product mindset - you think about who will use what you build, how it creates value, and what makes a solution genuinely usable and maintainable.
• You are a systems thinker - able to zoom out and view solutions as reusable capabilities or platform opportunities, not simply isolated developments.
• You have a strong debugging mindset - when things break, you are naturally curious and willing to investigate root causes across the stack rather than immediately escalating.
• You are a proactive communicator - you surface blockers, edge cases, technical debt, and architectural trade-offs early and clearly.
• You are curious about AI tooling - you do not need deep production AI experience, but you are interested in how modern AI capabilities are changing analytics engineering and are keen to apply them practically.
• You are willing to challenge constructively - if a proposed approach is unlikely to scale or introduces unnecessary complexity, you are comfortable flagging concerns and suggesting alternatives.
• You are comfortable balancing engineering quality with delivery pace - understanding when robustness matters most and when pragmatism is the better trade-off.

• Microsoft Fabric
• Python
• Advanced T-SQL
• Data modelling
• REST APIs
• Git/DevOps workflows
• CI/CD concepts and practices
• Azure (general platform knowledge and hands-on experience)
• React/TypeScript (working knowledge and willingness to contribute)
• Experience designing scalable data or analytics solutions

Desirable:

• React/TypeScript (strong hands-on experience with proven track record of delivery)
• Deploying Azure Container Apps and other Azure resources
• LLM/RAG implementation patterns
• Power BI, Semantic models + DAX
• DBT
• FastAPI, DuckDB, Polars
• Kimble methodologies
• YAML pipelines
• Microsoft Graph API
• Machine Learning (high-level conceptual understanding)
• Experience building internal operational or analytics tooling

Soft skills / ways of working

• Proactive and pragmatic: you take ownership, prioritise well, and focus on delivering outcomes - not just activity.
• Investigative mindset: you don't just rely on tried and tested methods - you are mindful of emerging tech, libraries and approaches as the wider data landscape evolves.
• Strong written and verbal communication: able to explain complex analysis and data concepts clearly to both technical and non-technical audiences, adapting style to the stakeholder.
• Confident engaging with stakeholders: comfortable gathering requirements, attempting to reduce ambiguity, translating needs into automated data solutions and are not afraid to challenge/push back if the requested outcomes don't meet our strategic principles; for example, if the proposed solution would struggle to scale, or perhaps would introduce significant ongoing manual overhead.

This role will suit you if:

• You are a pragmatic engineer - comfortable shipping working solutions, learning quickly, and iterating over time rather than over-engineering upfront.
• You are comfortable with ambiguity - requirements will not always be fully defined, and you are able to work backwards from desired outcomes and ask the right questions.
• You can operate across the data spectrum - happy moving between SQL, Python, infrastructure, architecture discussions, and frontend work without needing rigid boundaries between disciplines.
• You have a product mindset - you think about who will use what you build, how it creates value, and what makes a solution genuinely usable and maintainable.
• You are a systems thinker - able to zoom out and view solutions as reusable capabilities or platform opportunities, not simply isolated developments.
• You have a strong debugging mindset - when things break, you are naturally curious and willing to investigate root causes across the stack rather than immediately escalating.
• You are a proactive communicator - you surface blockers, edge cases, technical debt, and architectural trade-offs early and clearly.
• You are curious about AI tooling - you do not need deep production AI experience, but you are interested in how modern AI capabilities are changing analytics engineering and are keen to apply them practically.
• You are willing to challenge constructively - if a proposed approach is unlikely to scale or introduces unnecessary complexity, you are comfortable flagging concerns and suggesting alternatives.
• You are comfortable balancing engineering quality with delivery pace - understanding when robustness matters most and when pragmatism is the better trade-off.