Data Analyst

Sephora
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

At Sephora we inspire our customers, empower our teams, and help them become the best versions of themselves.  We create an environment where people are valued, and differences are celebrated. Every day, our teams across the world bring to life our purpose: to expand the way the world sees beauty by empowering the ExtraOrdinary in each of us.

We are united by a common goal -to reimagine the future of beauty.


 

The Opportunity

 

Analytics is the compass that guides Sephora on its strategic journey, and we are on the lookout for a Data Analyst who can harness the power of data to propel our business forward. As a Data Analyst, you'll play a critical role in analyzing and interpreting customer data to drive business decisions across our digital platforms. Leveraging your strong experience in BigQuery SQL, you'll craft scripts that seamlessly integrate back-end data with Google Analytics 4 for in-depth customer analysis and segmentation. 

 

Your expertise in data modeling will enable you to map out data structures that support the creation and maintenance of dashboards and reports, tracking key performance indicators (KPIs) related to online engagement, traffic, conversion rates, and marketing effectiveness. In this role, you'll work with data visualization tools like Power BI and Google Data Studio to develop compelling visual reports that convey complex insights in a clear and actionable manner. Additionally, you'll ensure the accuracy and quality of data, while effectively communicating your insights to non-technical stakeholders. 

 

You will also be responsible for: 

  • Writing and optimizing BigQuery SQL scripts for GA4 analysis, integrating back-end data to segment and analyze customer data. 

  • Developing and maintaining dashboards and regular weekly/monthly reports to track KPIs related to performance, online engagement, A/B testing, traffic, conversion rates, marketing attribution, and campaign effectiveness. 

  • Using data visualization tools such as Power BI and Google Data Studio to create clear, actionable insights for stakeholders. 

  • Engaging in data modeling to effectively map out data structures that support business goals. 

  • Utilizing digital analytics tools like Google Analytics and Quantum Metric to deliver data-driven recommendations. 

  • Ensuring the accuracy and quality of data while drawing insightful conclusions and making strategic recommendations. 

 

What You'll Bring  

 

Your passion for detail is matched by your logical thinking and curiosity. You not only obsess over details but also possess the agility to communicate improvements rapidly and clearly. You thrive in an ever-evolving landscape, where driving innovation and continuous improvement is paramount. We are looking for a detail-oriented Data Analyst with strong analytical skills, capable of working both independently and collaboratively within a dynamic team environment. 

We value ambition paired with a logical mindset—individuals who are curious, inquisitive, and able to articulate suggestions with precision. You will utilize your strong foundation in BigQuery SQL, particularly in GA4 analysis, coupled with hands-on experience in data visualization using tools like Power BI. Your deep understanding of digital analytics, especially within Ecommerce and Retail, will enable you to provide actionable insights that drive business success. Your proficiency in digital analytics tools such as Google Analytics and Quantum Metric will be essential in providing recommendations that optimize performance. 

 

Our ideal candidate will also possess: 

  • Proficiency in Google Analytics, Quantum Metric, or similar digital analytics platforms. 

  • Experience with Google Marketing Platform. 

  • Strong communication skills, both written and verbal, for conveying complex data insights to non-technical stakeholders. 

  • An eye for detail and a commitment to data accuracy and quality. 

  • Familiarity with Google Tag Manager, A/B testing tools, competitor research using market share tools, setting up data feed APIs, and UX platforms would be advantageous. 

 

 

While at Sephora, you’ll enjoy...

  • The people.You will be surrounded by some of the most talented leaders and teams – people you can be proud to work with.

  • The learning.We invest in training and developing our teams, and you will continue evolving and building your skills through personalized career plans.

  • The culture.As a leading beauty retailer within the LVMH family, our reach is broad, and our impact is global. It is in our DNA to innovate and, at Sephora, all 40,000 passionate team members across 35 markets and 3,000 stores, are united by a common goal - to reimagine the future of beauty.

You canunleash your creativity,because we’ve got disruptive spirit. You canlearn and evolve,because we empower you to be your best. You canbe yourself,because you are what sets us apart.This,is the future of beauty. Reimagine your future, at Sephora.

Sephora is proud to be an equal opportunity workplace for all. We do not discriminate in recruitment, hiring, training, advancement, or other employment practices. We celebrate diversity and are committed to creating and fostering an inclusive environment for all employees.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.