Senior Analytics Engineer

Materialbank; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Material Bank in London is seeking a Senior Analytics Engineer to own analytics data models, dashboards, and the semantic layer that supports our teams and brand partners.

You'll build and maintain dbt models in Snowflake, ensure metric truth, create high-value dashboards in Tableau (and Sigma as the stack evolves), and collaborate across functions to deliver trusted insights.

Qualifications

• 5+ years in analytics engineering or a similar technical analytics role.
• Expert SQL on large datasets with ability to debug others' logic.
• Strong Snowflake and dbt experience, including dimensional modeling, testing, and documentation.

Responsibilities

• Own the analytics engineering for assigned business domains, translating metric definition through data model, dashboard, and insight.
• Design, build, and maintain production-quality dbt models in Snowflake that turn metric definitions into reliable models and semantic definitions.
• Identify and resolve technical inconsistencies in metric logic so the business runs on one certified source of truth.
• Build a small set of high-value dashboards in Tableau (and Sigma) as the stack evolves.
• Contribute to the semantic layer so certified metrics are reusable across BI tools, Snowflake Cortex, and embedded analytics.
• Automate recurring analytics work, with tests and monitoring to keep it trustworthy.
• Partner with analysts and stakeholders to interpret data, build context, and surface risks.
• Contribute to brand-facing data products used by account teams and manufacturer partners.

Skills

Ownership

End-to-end

SQL

Snowflake

dbt

Tableau

Sigma

Looker

Communication

Stakeholder management

Tools

Snowflake

dbt

Tableau

Sigma

Looker

Job description

Material Bank is the world's largest material marketplace for the architecture and design industry. Operating in 37 countries, our platform has become the standard for design professionals around the globe. Every day, Material Bank connects thousands of designers with tens of thousands of materials from leading brands. Material Bank is the fastest and most powerful way for design professionals to search, sample, and specify materials.

Senior Analytics Engineer, Analytics & Insights

London, UK (Hybrid, 2 days in office)

About the role

We are looking for a Senior Analytics Engineer to join Analytics & Insights, the team that owns Material Bank's analytics data layer, internal reporting, and data products that support our teams and brand partners.

This is a hands‑on technical role for someone who wants to own how analytics get built. You will take approved metric definitions and business questions and turn them into the models and metric logic in Snowflake and dbt, the dashboards, and the semantic layer and data products people use every day.

You will own the analytics engineering for key areas of the business: understanding what needs to be measured, building the models, validating the logic, delivering the reporting, and keeping it accurate and reliable over time. You will work across multiple departments and across our data products, including embedded analytics for brands. The work varies day to day, and as a team we contribute to everything.

What we require

• Ownership, end to end. You own what you build, from the first stakeholder conversation to the model, the dashboard, and the fix when something breaks.
• Precision and attention to detail. You understand the nuance behind a number, verify before you publish, and produce work that leaders and brand partners can trust without checking.
• Appetite for hard work. We are a small, ambitious team with a lot of goals. The work is demanding and the hours vary with what needs to get done. We are upfront about that because it is what the role is.

What you'll do

• Own the analytics engineering for assigned business domains, translating metric definition through data model, dashboard, and insight.
• Design, build, and maintain documented, production‑quality dbt models in Snowflake that turn approved metric definitions into reliable, reusable models and semantic definitions, and power reporting, self‑serve, automation, and AI.
• Identify and resolve technical inconsistencies in metric logic so the business runs on one certified source of truth.
• Build a small set of high‑value dashboards in Tableau (and Sigma, as we evolve the stack).
• Contribute to the semantic layer so certified metrics are reusable across BI tools, Snowflake Cortex, and embedded analytics.
• Automate recurring analytics work, with the tests and monitoring that keep it trustworthy.
• Partner with analysts and stakeholders to interpret data, build context, and surface risks before they are asked.
• Contribute to brand‑facing data products used by account teams and manufacturer partners.

What you'll bring

5+ years in analytics engineering, data analytics, or a similar technical analytics role, owning data models and reporting that a business relies on.

Expert SQL on large and complex datasets. You write clean, efficient queries and can debug someone else's logic as easily as your own.

Strong hands‑on Snowflake and dbt (or a comparable modern data stack), including dimensional modeling, incremental models, testing, and documentation.

Strong Tableau skills. Sigma, Looker, or similar a plus.

Strong judgment on metric design: grain, denominators, filters, time zones, deduplication, and the ways a correct number can still mislead.

Commercial sense and curiosity. You want to understand why a number matters, can explain what a change means for the business, and hold a position under questioning.

An automation mindset. When you see something repetitive or fragile, your instinct is to find a better way to build it.

Comfort working across several domains at once, prioritizing your own work, and managing stakeholders directly.

Clear, concise communication and the ability to explain technical concepts to non‑technical audiences.

Preferred

Helpful, not required:

• Python for automation and data validation
• Marketplace, e-commerce, or B2B SaaS analytics
• Event and behavioral data (Segment, GA4, or similar)