wgsn - Senior Analytics Engineer

• The WGSN Data team is a vibrant, multicultural mix of brilliant minds, blending global perspectives with deep expertise in retail and consumer analytics across a diverse range of industries such as fashion, beauty, interiors, and FMCG. Driven by a spirit of collaboration, we leverage our varied skill sets to transform complex datasets into high-impact insights, enabling our stakeholders to make confident, actionable, and results-driven business decisions. • Key accountabilities • 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. • This list is not exhaustive and there may be other activities you are required to deliver. • Skills, experience & qualifications required • 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.