Senior Data Analyst

About the role

The role partners with FP&A and Finance to guide commercial choices and long-term planning for Soho House.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

The Data & Analytics team grows with commercial strategy embedded in daily operations.

The position collaborates closely with FP&A and senior stakeholders on growth decisions.

Finance partnerships focus on building new management reporting and owning core KPIs.

A core responsibility is enabling AI tools with clean, modeled data for self-service insights.

What you'll do

Analysis of market performance and member behaviour steers investment choices and location expansion.

Automated performance dashboards and maintained KPIs power executive reviews through trading insights and operational reporting.

Data models and pipelines built end-to-end in dbt deliver decision metrics alongside Finance for management reporting.

Requirements

3+ years of data analyst experience with a record of actionable insights for business stakeholders.

Strong SQL skills for manipulating and interpreting large, complex datasets to drive insights.

Hands-on data development experience with solid data modeling and analytics pipelines built end-to-end.

Clear communication and stakeholder engagement turn complexity into actionable insight for trusted partners.

A strategic mindset with strong commercial instincts linking business operations to data .

Adaptability to switch between strategic analysis and operational reporting across varied priorities.

A growth mindset that challenges assumptions and pushes boundaries beyond task execution.

Nice to have

Experience with BI tools and notebooking environments such as Omni and HEX.

Hands-on dbt experience for building and maintaining transformations.

Familiarity with ERP or finance systems like Oracle or SAP for reporting transformation.

Practical experience with AI tools such as Claude used in day-to-day work.

Skills & tools

SQL, dbt, BI tools, AI tools, data modeling, pipeline development.

Practical notes

The role is based in London with defined onsite expectations as set by local policy.

Role involves collaboration across Finance, FP&A, and executive stakeholders.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data analyst roles typically combine quantitative analysis with stakeholder communication to turn data into decisions.

Familiarity with modern data stacks such as warehouses, transformation layers, and visualization tools is common in this field.

Business intelligence and AI enablement are growing areas where clean data foundations support self-service and automation.

Roles embedded in commercial teams often balance strategic planning with day-to-day operational reporting.

Continuous learning through structured training supports long term career growth in analytics.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.