Analytics Engineer, Data & AI Platform

• 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.

• 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.

At Monica Vinader, we believe luxury should be empowering, long-lasting and responsibly made. Guided by integrity, craftsmanship and innovation, our goal is to elevate people’s lives by opening access to a more beautiful world.

From crafting consciously with recycled precious metals and ethically sourced materials, to designing enduring, versatile pieces made to be layered, loved and lived in every day, we are redefining what modern jewellery can be. We create jewellery that marks moments, tells stories and becomes part of who you are, all while making responsible luxury more accessible.

Our commitment to sustainability, innovation and positive impact continues to be recognised across the industry.

We are proud to have received:

• Responsible Jewellery Brand, 2026 - Country & Town House
• Responsible Business of the Year, 2025 - Positive Luxury
• Top 50 Inspiring Workplaces (UK & NI), 2024 - Inspiring Workplaces

With a global footprint across physical retail, e-commerce and trusted partners, we put our community at the heart of everything we do. Proudly female-founded and inclusive, we build meaningful relationships with the people who wear and love our jewellery.

We are looking for an Analytics Engineer to join us as we continue this journey and help us shape what the future of modern jewellery can and should be.

Where we need your help

Monica Vinader is on a journey to become truly AI-native - one where decisions improve with every cycle and our data works as hard for us as our products do. We’ve already proven the approach at small scale: our trading data is queryable in plain English, our teams are using AI-built apps, and AI is woven into how the data team builds day to day.

The next step is the foundation. As Analytics Engineer, you’ll own the craft and standards of a well-governed data and AI platform: the models, the ingestion, the semantic layer, and the engineering discipline that keeps definitions and data from drifting apart - so that every query, whether asked by a person or an AI agent, gets answered the same way.

This is an engineering role at heart. You’ll own the how - the standards, the workflows, the quality bar - and have genuine input into the what, the architecture we build towards. You won’t be the dashboard builder; you’ll build the road the dashboards, apps and conversational analytics run on, and the governed building blocks the rest of the team uses to build on top.

You will have real ownership of the platform’s foundations, and the space to shape how a modern, AI-first data team operates. If you thrive at the intersection of engineering rigour and analytical thinking, want your work to have direct commercial impact, and are excited about a world where AI is a core part of how you build, this is the role for you.