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MOO

Posted 6 days ago

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Senior Analytics Engineer

LondonHybrid

AI Summary

Owns analytical domains end-to-end, defining metrics in the semantic layer, modelling data in dbt, and ensuring trusted KPI answers across dashboards, self-service tools, and AI assistants.

About this role

MOO brings brands to life in a sustainable way, with a range of remarkable print and merchandise products. We combine design, technology, manufacturing and service, so that people can connect in memorable ways. Not online but out there, in real life.
We started in 2004. Since then we’ve built an award-winning and much-loved brand, with customer satisfaction and Trustpilot ratings that make most businesses want to give up and hire an army of review bots. We’ve got half a million customers, mostly small and medium businesses in North America, the UK, and Europe – businesses that, like us, get all excited about putting something real and beautiful into people’s hands. Does that make us nerds? Probably, and we’re ok with that.
We’ve been given the highest business award in Britain, ‘The Queen’s Award for Enterprise’. Backed by venture capital, we’re part of Tech Nation’s ‘Future Fifty’, recently passing $1bn in lifetime revenue, and featuring in the Guardian’s top 10 UK start-ups list. Ok, we’ll stop bragging now.
Today, we’re more than 400 people with our global HQ in London, UK, while we also have premises in Dagenham. In the US, you’ll find us in Boston, MA, as well as East Providence, RI and Denver, CO and with our most recent office expansion in Cape Town South Africa.

We're a team of data engineers and analytics engineers running the platform that brings together data from our e-commerce, manufacturing, fulfilment, and finance systems.

We build on a modern, warehouse-native stack — Snowflake, dbt, and Dagster — and we're working towards a governed semantic layer so trusted metrics are defined once and consumed everywhere: dashboards, self-service tools, and AI assistants.
This role exists to own analytical domains end-to-end. Defining their metrics in the semantic layer with stakeholders, data modelling in dbt, and making sure a KPI resolves to the same trusted answer wherever it's consumed.

The person we want

You're an analytics engineer or data analyst with a rounded experience in data modelling and engineering practice.

You possess excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.

You have strong business acumen and instinct, enabling you to challenge metric definitions to ensure they reflect real business outcomes

You hold an honest, practical view about AI. You're enthusiastic about what governed, semantically-modelled data makes possible, but rigorous about validation and approach new capability with a healthy skepticism (but not cynicism).

Responsibilities

  • Partner with stakeholders across operations, commercial, finance, and supply chain to design data models and unlock analytic capabilities
  • Define metrics, dimensions, and business logic in the semantic layer (dbt Semantic Layer / Snowflake semantic views)
  • Deliver reporting through our BI tooling (currently Tableau) with a tool-agnostic mindset, and reduce duplicated or conflicting outputs.
  • Curate and validate governed datasets and semantic models for AI/natural-language consumption, acting as the accuracy bar for AI-generated analysis.
  • Champion healthy self-service and data literacy heading in the directon of fewer, better, trusted outputs
  • Review others' work constructively and contribute to the team's modelling standards.
  • Demo new features and train business stakeholders when required

    • About you

  • Strong SQL and production dbt experience
  • Production experience on a cloud warehouse with Git-based, review-first workflows
  • A track record of defining metrics with stakeholders and delivering outcomes people rely on
  • Can demonstrate sound judgement about where logic should live - e.g. in semantic layer or BI layer
  • Demonstrable interest in how data analytics is changing alongside AI, and developing own skillset

    • Nice to have's

  • Semantic layer tooling in production
  • Natural-language/AI analytics tools (e.g. Cortex Analyst or similar) or preparing data for LLM consumption.
  • Experience rationalising dashboard estates or migrating BI logic downstream
  • E-commerce, manufacturing, or subscription business domains

    • Skills

    Cortex AnalystDagsterData ModellingDbtDbt Semantic LayerGit-based WorkflowsLLM Data PreparationNatural-language AnalyticsSemantic LayerSnowflakeSnowflake Semantic ViewsSQLTableau

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