Senior Analytics Engineer

We are looking to hire a Senior Analytics Engineer to join our Data Science team in London.

This is a hybrid role based out of our London office. A minimum of 3 days per week in office is required.

A career with WGSN is fast-paced, exciting and full of opportunities to grow and develop. We're a team of consumer and design trend forecasters, content creators, designers, data analysts, advisory consultants and much more, united by a common goal: to create tomorrow. 

WGSN's trusted consumer and design forecasts power outstanding product design, enabling our customers to create a better future. Our services cover consumer insights, beauty, consumer tech, fashion, interiors, lifestyle, food and drink forecasting, data analytics and expert advisory. If you are an expert in your field, we want to hear from you.

At WGSN, we believe that data is only as valuable as the decisions it empowers. As a Senior Analytics Engineer, you will sit at the vital intersection of Data Analysis and Data Engineering. Your mission is to bridge the gap between raw data and commercial value by building the internal tools, prototypes, and scalable data models that fuel our business intelligence.

Unlike traditional data engineering roles focused on heavy production infrastructure, you will focus on agility—leveraging modern tech stacks and AI acceleration to spin up prototypes and methodologies quickly then scale dashboards and tools over time.

As a Senior member of the team, you will be the champion of data best practices. You will initially focus on technical leadership as an individual contributor and mentoring other analysts to elevate their data modeling and software engineering skills. Over time, this role is expected to transition into direct line management responsibilities as the team expands.

• Scalable Data Modeling: Design, build, and maintain robust, modular data models within Snowflake using dbt core, ensuring they are optimized for performance and aligned with business needs.

• Prototyping & Internal Tools: Rapidly build interactive GUIs, internal data applications and dashboards, and prototypes using Streamlit or similar to get actionable tools into the hands of stakeholders.

• Software Engineering Best Practices: Enforce modern software development standards across the analytics team, including rigorous version control (GitHub), CI/CD pipelines, and data testing paradigms.

• Data Integrity: Take ultimate ownership of data quality, cleaning, and transformation processes to ensure the wider business operates on a single source of truth.

• Team Mentorship: Actively coach and mentor data analysts, guiding them in writing production-grade SQL, adopting Python, and embracing dbt best practices.

• Culture of Learning: Foster an environment of continuous learning, specifically helping the team explore more advanced technologies.

• Translate Business to Tech: Partner with non-technical business leaders and technical engineering teams alike to translate complex commercial needs into working data solutions.

• Advocacy & Sharing: Proactively showcase new data products, share analytical insights with the wider business, and vocally champion data-driven decision-making in cross-functional meetings.

• Agile Execution: Manage your work and dependencies independently using JIRA within a sprint framework, remaining highly adaptable to changes in scope or project requirements.

• Data Warehousing: Advanced proficiency with Snowflake and writing highly efficient, complex SQL queries.

• Scripting & Modeling: Strong Python development skills for data manipulation and hands-on experience with data modeling (dbt core)

• UI/UX & Frontend: A proven track record of building user-friendly internal tools. A deep understanding of what makes a practical, intuitive UI/UX is what will set you apart. Experience with Streamlit is highly preferred (React is a plus).

• AI-Augmented Development: Comfort and experience using modern AI agents (e.g., VS Code, Cursor, Codex, or similar tools) to accelerate code generation, prototyping, and debugging.

• Advanced Data Tech: Exposure to or strong interest in data science concepts, machine learning workflows, vectorization/embeddings, and graph databases.

• Project Management: Comfortable working inside JIRA, managing sprints, and proactively calling out project dependencies.

• Empathetic Communicator: You are comfortable presenting to senior leadership one hour and debugging a query with a junior analyst the next. You know how to make technical concepts clear to anyone.

• 5-8 years of experience in Analytics Engineering or a combination of Data Analytics and Data Engineering required.

•  The "Follow Your Nose" Mentality: You possess an analytical mind fueled by curiosity and resourcefulness. When data looks strange or an opportunity is hidden, you have the instinct to dig deep and simplify complex information.