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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer, Data & AI Platform - **Company:** Monica Vinader Ltd - **Location:** London, UK - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Business Logic, BigQuery, Code Generation, Information Engineering, Data Infrastructure, Data Transformation, Software Tools, Cloud Services, DataOps, SQL Databases, Scripting, Google Data Studio, Git, Data Layers, AI Platforms, Git Flow, Software Version Control, Api Management - **Published:** July 26, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=b7a180111c7e4bba ## About the Role * Strong command of SQL and hands-on experience building data transformation models, preferably in dbt. * Hands-on experience with cloud data platforms, preferably GCP and BigQuery, with an eye for cost and performance. * Experience setting up ingestion - automated connectors (e.g. Meltano), custom Python scripts, API integrations. * Comfortable with version control and Git-based workflows: branching, pull requests and reviews. * A solid grasp of data quality, testing, observability and documentation - with documentation treated as part of the model, never an afterthought. * Familiarity with semantic layers and metric governance is a plus. * Genuinely fluent using AI tools in your own workflow (code generation, review, prototyping, documentation), curious about how AI is changing the data landscape, and comfortable that your work will increasingly be consumed by AI agents as well as people. * Exposure to BI tools such as Sigma or Looker Studio is useful. A background in retail, DTC or e-commerce is strongly preferred. Connect & Empower * A collaborative communicator who can bridge technical complexity and business need, adapting your style to both technical and non-technical audiences. * A business head as well as an engineering one: you understand the question behind a request, and you'll push back constructively when a request isn't the right way to solve the underlying problem. * Willing to share knowledge, mentor peers and contribute to a supportive data community. Drive & Deliver * A proactive mindset - you take ownership, spot inefficiencies and drive improvements without waiting to be asked. * Strong attention to detail, particularly around data quality, governance and testing. * Able to manage your own workload, balance competing priorities and deliver end-to-end, from scoping through to sign-off. Grow & Adapt * Comfortable in a fast-paced, high-growth environment where priorities can shift and pragmatism is valued. * A genuine curiosity for learning - new tools, techniques and business domains. * Open to feedback and reflective about how to improve your own approach. ## Description * Build, maintain and optimise data transformation models in dbt on BigQuery - clean, reusable and well-documented - that power reporting, dashboards, apps and conversational analytics. * Keep business logic where it belongs: in the modelling layer, not baked into front-end tools or dashboards - so a metric means the same thing on every surface. * Contribute to the direction of our data model Ingestion & the data platform * Set up and maintain data ingestion from third-party sources, using both automated connector platforms (tools such as Meltano) and custom Python scripts in GCP for bespoke sources - competitor scraping, operational feeds, people data and similar. * Own the warehouse craft on GCP and BigQuery: cost management, query efficiency, model refresh strategies, orchestration and dependency management. * Keep the pipes reliable and readable - for both people and AI agents. This is plumbing you own, sized so that maintaining it is the smaller half of your week, not the whole of it. Semantic layer & metric governance * Help run our semantic layer as a product: definitions agreed once, versioned, and answered consistently wherever they surface. * Certify and deprecate: one trustworthy definition per metric, reconciled before it counts, with near-duplicates retired on a schedule. * Make sure figures can be traced back to their source, so the numbers our teams and tools produce are trusted. DataOps, quality & governance * Bring software-engineering discipline to data: changes ship as pull requests with automated checks, tests and clear documentation, using our Git-based workflow. * Use AI to accelerate building and reviewing changes. * Own how our data platform and internal data apps are hosted and deployed - building for reliability, security and scale on GCP. * Champion data quality - validation, alerting, monitoring and a growing regression suite of "golden questions" - to catch and resolve issues before they reach stakeholders. * Build with security and data privacy in mind (e.g. GDPR), with governance that is proportionate to our scale: structured enough to be safe, light enough to stay fast. Working with AI & continuous improvement * Use AI tooling as a core part of how you work - Claude Code as a default for building and reviewing, alongside other tools - not as an occasional accelerant. * Contribute to prototyping new ways of delivering data to the business: conversational analytics, agentic workflows and alternative front-end interfaces. * Interrogate the request before you build it. Understand the business question behind an ask, agree the spec and how you'll test it, then build. The measure of this role is what the platform lets others answer - not the number of tickets closed. * Stay close to emerging practice in data engineering and AI, and share what you learn with the team. ## Related Videos - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Why Git Still Matters](https://www.wearedevelopers.com/videos/100288-why-git-still-matters) - [Applying DevOps in Flutter mobile development](https://www.wearedevelopers.com/videos/60-applying-devops-in-flutter-mobile-development) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How to Turn Community Events Into a Powerful AI GTM Engine: The Daytona Playbook](https://www.wearedevelopers.com/magazine/732-how-to-turn-community-events-into-a-powerful-ai-gtm-engine-the-daytona-playbook)