Product Data Coordinator, Soho Home

About the role

This role supports product data management and governance for Soho Home during a 6 month FTC assignment in London. The position feeds an enhanced reporting environment by maintaining accurate, consistent, and scalable product information.

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

What you'll do

SKU creation across projects receives support to achieve accurate and timely setup for new items.

Workload management aligns with critical path timelines to meet project delivery commitments.

Product hierarchies are executed across existing SKU portfolios to standardize classification.

Data checks and reconciliations compare documentation against systems to highlight and resolve discrepancies.

Organized and structured data files and documentation are maintained to support retrieval and governance.

Requirements

Experience in data cleansing and standardisation applies to large data sets encountered in this role.

Highly organised work is delivered with strong attention to detail and accuracy on data tasks.

Established Excel skills such as formulas, data organisation, Vlookups, data validation, and pivot tables are used.

Problem solving and execution of project priorities occur within structured timelines and priorities.

Established and confident communication skills support collaboration and clarity.

At least 1+ years of relevant or equivalent demonstrated experience is required for this position.

Problem-solving ability identifies and resolves issues related to data management in this scope.

Effective relationship building with cross-functional partners enables cooperation across teams.

Nice to have

Experience or interest in product data, Merchandising systems, or ERP platforms is desirable for this role.

Familiarity with Power BI and Business Central supports reporting and data analysis.

Interest in Retail operations, Buying, or Product Development/data environments adds context.

Working with Airtable database or similar database software assists with data organisation.

Experience using PIM or PLM systems helps manage product information lifecycles.

Practical notes

This is a 6 month FTC assignment based in London, England, United Kingdom, requiring collaboration with the Buying & Product Development team.

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

Roles in this field focus on maintaining clean, consistent, and governed product information across systems.

Common tools include spreadsheets, data validation processes, and reporting platforms used to track product hierarchies.

Success depends on attention to detail, structured problem solving, and clear communication with cross-functional stakeholders.

Work often involves reconciling discrepancies between documentation and live system data.

Opportunities exist for professional growth through training and exposure to merchandising and product data environments.

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.