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About the Role

Monica Vinader is seeking an Analytics Engineer to own the craft and standards of a well-governed data and AI platform, including models, ingestion, and the semantic layer. This engineering role focuses on establishing standards, workflows, and quality bars for the platform, with input into its architecture. The position offers ownership of the platform's foundations and the opportunity to shape how a modern, AI-first data team operates.

Responsibilities

Build, maintain, and optimize data transformation models in dbt on BigQuery.
Keep business logic in the modeling layer, ensuring consistent metric definitions across all surfaces.
Contribute to the direction of the data model.
Set up and maintain data ingestion from third-party sources using automated connectors and custom Python scripts.
Own warehouse craft on GCP and BigQuery, including cost management, query efficiency, model refresh strategies, and orchestration.
Ensure data pipelines are reliable and readable for both people and AI agents.
Help run the semantic layer as a product, ensuring agreed-upon definitions are versioned and consistently applied.
Certify and deprecate metrics to maintain a single, trustworthy definition per metric.
Ensure figures can be traced back to their source for trusted data.
Apply software engineering discipline to data, using pull requests with automated checks, tests, and documentation.
Use AI to accelerate building and reviewing changes.
Own the hosting and deployment of the data platform and internal data apps on GCP.
Champion data quality through validation, alerting, monitoring, and a regression suite.
Build with security and data privacy in mind, implementing proportionate governance.
Use AI tooling as a core part of the workflow for building, reviewing, and prototyping.
Contribute to prototyping new ways of delivering data, such as conversational analytics and agentic workflows.
Understand the business question behind a request, agree on the specification, and define testing methods.
Stay informed about emerging practices in data engineering and AI, and share knowledge with the team.

Requirements

Strong command of SQL.
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, including automated connectors, custom Python scripts, and API integrations.
Comfortable with version control and Git-based workflows (branching, pull requests, reviews).
Solid grasp of data quality, testing, observability, and documentation.
Familiarity with semantic layers and metric governance is a plus.
Genuinely fluent using AI tools in workflow (code generation, review, prototyping, documentation).
Curious about how AI is changing the data landscape.
Comfortable with work being 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.
Collaborative communicator who can bridge technical complexity and business needs.
Adapt communication style to both technical and non-technical audiences.
Possess a business head as well as an engineering one.
Willing to share knowledge, mentor peers, and contribute to a supportive data community.
Proactive mindset; take ownership, spot inefficiencies, and drive improvements.
Strong attention to detail, particularly around data quality, governance, and testing.
Able to manage own workload, balance competing priorities, and deliver end-to-end.
Comfortable in a fast-paced, high-growth environment where priorities can shift.
Pragmatism is valued.
Genuine curiosity for learning new tools, techniques, and business domains.
Open to feedback and reflective about improving own approach.
Hands-on, solutions-focused, and entrepreneurial.
Collaborate openly with humility, honesty, and humour.
Embrace learning, teaching, and personal growth.
Stay resilient, adaptable, and self-motivated in a creative environment.
Speak up when you don’t know and act fast to figure it out.
Ability to document authorization to work in the United Kingdom.

Skills

SQL
dbt
BigQuery
GCP
Python
API integrations
Git
Data quality
Testing
Observability
Documentation
Semantic layers
Metric governance
AI tooling
Conversational analytics
Agentic workflows
Sigma
Looker Studio

Location

London (Hybrid)

Work Type

Hybrid

Experience Level

Analytics Engineer

About the Company

Monica Vinader believes luxury should be empowering, long-lasting, and responsibly made, aiming to elevate people’s lives by opening access to a more beautiful world.
The company crafts jewellery consciously with recycled precious metals and ethically sourced materials, designing enduring, versatile pieces.
Monica Vinader is redefining modern jewellery, creating pieces that mark moments, tell stories, and become part of who you are, while making responsible luxury more accessible.
The company has received recognition for sustainability, innovation, and positive impact, including awards like 'Responsible Jewellery Brand' and 'Responsible Business of the Year'.
With a global footprint across physical retail, e-commerce, and trusted partners, Monica Vinader prioritizes its community and fosters meaningful relationships.
It is a proudly female-founded and inclusive company.

Equal Opportunity

Monica Vinader makes the following inclusive culture pledge: Our jewellery is for everyone and so is our community. Together, we will continue to implement sustainable changes to ensure that career opportunities and progression are open to all. We commit to celebrating the diverse voices of our employees, partners, and the customers we serve.