At a Glance
- Tasks: Own analytics engineering, build models, and create insightful dashboards for data-driven decisions.
- Company: Join Material Bank, the leading material marketplace for architecture and design professionals.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative team culture with diverse projects and career advancement opportunities.
- Why this job: Make a real impact in a fast-paced environment with cutting-edge analytics tools.
- Qualifications: 5+ years in analytics engineering, expert SQL skills, and strong experience with Snowflake and dbt.
The predicted salary is between 72000 - 88000 £ per year.
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 Saa S analytics
- Event and behavioral data (Segment, GA4, or similar)
- #J-18808-Ljbffr
Senior Analytics Engineer employer: Materialbank
Material Bank is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets collaboration. As a Senior Analytics Engineer, you'll have the opportunity to take ownership of impactful projects while working alongside a passionate team dedicated to transforming the architecture and design industry. With a strong focus on employee growth, hybrid work flexibility, and a culture that values precision and hard work, Material Bank empowers you to thrive both personally and professionally.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Materialbank!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Analytics Engineer at Materialbank.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Materialbank.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Analytics Engineer at Materialbank, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Analytics Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Materialbank, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Materialbank. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Materialbank
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Materialbank!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
