Head of Data

Club L London

Manchester

On-site

GBP 120,000 - 160,000

Full time

29 hours ago

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Benefits offered by this job

Annual bonus

Dress allowance

25 days annual leave

Extra day off for birthday

Flexible working hours

Early Finish Fridays

Cycle to work

Staff discount

Healthcare cashplan

Gym on site

Enhanced pension

Maternity/Sick pay

Snacks and drinks

Social events

Job summary

Club L London is seeking a Head of Data to own the data strategy, platform, and team, reporting to the CTO. You will transform the data estate into reliable, self‑serve insights powering decisions across merchandising, marketing, and operations.

You will lead analytics engineers and analysts, shape the modern data stack (BigQuery, dbt, semantic layer), and uphold data quality, governance, and cross‑functional collaboration to drive commercial value.

Qualifications

5+ years in data leadership or building analytics teams.
Proven experience in retail/e‑commerce or high-traffic digital environments.
Strong SQL skills with modular, well‑structured transformations.
Deep experience with dbt, data modelling, and semantic layers.
Experience with BigQuery or similar cloud data warehouses.
Ability to mentor and grow analytics engineering capability.

Responsibilities

Define and lead the data strategy, roadmap, and operating model.
Oversee transformation and semantic layers in the data stack (dbt models in BigQuery).
Ensure clean, governed, well‑modelled datasets powering BI dashboards (Power BI).
Lead data analysts and analytics engineers to deliver high‑value insights.
Establish data contracts, governance, and quality frameworks across teams.
Foster a self‑serve culture and enable AI/LLM‑driven analytics.

Skills

Data leadership

SQL

BigQuery

dbt

Power BI

Data governance

Tools

BigQuery

dbt

Power BI

Git

Job description

About Us

Club L London is the next-generation online fashion retailer for the forward-thinking woman. Conceptualised and crafted in-house and abroad, we specialise in accessible luxury and designs of unrivalled quality that flatter all figures. From prom to occasion, maternity, bridal, and beyond, we deliver an elevated shopping experience that connects our global community of trend-setting consumers, influencers, and content creators with fresh collections dropping weekly. Data sits at the heart of how we merchandise, market, and operate across multiple markets - and we're investing to make it a genuine competitive advantage.

The Role

We are looking for a Head of Data to own our data strategy, platform, and team, and to turn our data estate into trusted, self‑serve insight that powers decisions across the business. Reporting to the CTO and partnering closely with the development team, you'll lead the Data function – currently our Analytics Engineer and Data Analysts – and own everything that happens once data lands in the warehouse: transforming, modelling, testing, documenting, and serving it as governed, reliable datasets.

This is a hands‑on-when-it‑counts leadership role. You'll set the direction for our modern data stack (BigQuery, dbt, and a governed semantic layer), raise the bar on data quality and governance, and structure the platform so both people and AI‑powered tools can self‑serve trusted answers at scale. You'll grow the team's capability, own the relationship with data stakeholders across the business, and make data a dependable foundation for reporting, analytics, and merchandising intelligence.

Key Responsibilities

Data Strategy & Leadership

Own and evolve the data strategy, roadmap, and operating model, aligning it with commercial and technology priorities.
Lead the Data function, setting direction, standards, and priorities across analytics engineering and analysis.
Own the data budget, tooling estate, and vendor relationships, driving value and consolidation.
Report on data delivery, adoption, and impact to the CTO and wider leadership.

Data Platform & Engineering

Own the transformation and semantic layers of the data stack, directing the design and maintenance of dbt models across staging, intermediate, and mart layers within BigQuery.
Define and govern a dbt Semantic Layer - a single source of truth for core metrics (revenue, churn, LTV, and more) consumed consistently across BI tools and AI assistants.
Manage the data platform as code - Git‑based version control, CI/CD pipelines, environment management, and pull‑request workflows.
Optimise BigQuery for performance and cost through incremental models, partitioning, clustering, and efficient materialisation strategies.
Partner with the development team, who own extraction and loading, to evolve pipelines from schedule‑based to event‑driven orchestration, so transformations run when upstream data is ready.

Analytics, BI & Insight

Ensure the business has clean, governed, well‑modelled datasets powering Power BI dashboards and self‑serve analytics.
Establish consistent, trusted metric definitions so reporting is reliable and comparable across teams.
Direct the Data Analysts in delivering high‑value analysis and insight, from trading and merchandising through to marketing and operations.
Champion a self‑serve culture, reducing reliance on ad‑hoc requests and putting trusted data in the hands of decision‑makers.

Data Quality, Governance & Documentation

Implement robust data testing and quality frameworks using dbt tests, custom assertions, and automated alerting to catch issues before they reach stakeholders.
Own comprehensive documentation and lineage, ensuring every model, column, and metric is described, discoverable, and traceable back to source.
Establish data contracts with upstream teams, aligning on schema changes, source‑system updates, and clean handoffs.
Own data governance, access, and compliance for the estate, working with security and privacy stakeholders as needed.

AI‑Powered & Advanced Analytics

Structure the semantic layer so AI and LLM‑based tools can reliably translate natural‑language questions into correct, governed queries.
Support the adoption of AI‑powered analytics and natural‑language query tools, safely and with the right guardrails.
Identify and lead advanced analytics opportunities – forecasting, segmentation, and merchandising intelligence – that drive commercial value.

Team Leadership & Capability Building

Lead, mentor, and grow the Data team, building a culture of analytics engineering across analysts and engineers.
Upskill the team in dbt, SQL best practice, data modelling, and Git‑based workflows.
Own resourcing, hiring, objectives, and development, and grow the next layer of data talent.

Cross‑Functional Collaboration & Stakeholder Management

Work closely with the development team on data contracts, schema changes, and orchestration.
Partner with e‑commerce, merchandising, marketing, finance, and operations to understand needs and deliver data that moves the business.
Communicate clearly with both technical and non‑technical audiences, translating data into decisions.

More About You

5+ years in data and analytics, with several years leading or building data teams, ideally within retail, e‑commerce, or high‑traffic digital environments.
Proven data leadership – setting strategy, owning a platform, and growing team capability – while remaining technically credible and hands‑on when needed.
Strong SQL skills, with a track record of modular, well‑structured transformations.
Deep experience with dbt (models, tests, documentation, packages, incremental materialisation, and the semantic layer).
Experience with BigQuery or other cloud data warehouses, including performance and cost optimisation.
Strong grasp of data modelling techniques (dimensional modelling, star schemas, wide analytics tables).
Solid understanding of Git‑based workflows and CI/CD for analytics code.
Experience with BI tools (Power BI preferred) and how governed data models serve downstream reporting.
Familiarity with orchestration tools (Airflow, Prefect, or Dagster) and event‑driven pipeline patterns.
A genuine interest in mentoring and coaching, and building analytics engineering practice across a team.
Comfortable working cross‑functionally with analysts, developers, and senior business stakeholders.

Nice To Have

Awareness of how AI and LLM‑based tools interact with structured data and semantic layers, and experience enabling natural‑language query tools.
Experience with Python for data utilities, scripting, or lightweight services.
Experience with MongoDB or other NoSQL sources as upstream inputs.
Exposure to data observability platforms.
Experience across multi‑brand, multi‑market, or multi‑entity data estates.
Familiarity with the wider GCP ecosystem and modern e‑commerce data sources (e.g. Shopify, marketing, and CDP platforms).
Experience owning data governance and privacy in a regulated (UK GDPR) context.

What's on offer?