At a Glance
- Tasks: Own and scale our data platform, ensuring high-quality analytics and self-serve culture.
- Company: Join a leading consulting firm that empowers decision-making through data.
- Benefits: Enjoy competitive salary, health insurance, remote work flexibility, and generous annual leave.
- Other info: Be part of an early data team with room to innovate and grow.
- Why this job: Make a real impact on business decisions with cutting-edge data tools and AI innovation.
- Qualifications: Strong SQL skills, experience with dbt, and a passion for data quality.
The predicted salary is between 50000 - 70000 £ per year.
Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employees, customers, and other stakeholders. Consulting and private equity firms come to Sapient when they need to discover knowledge to help them make great decisions and succeed in their goals. It is our mission to support them in their discovery of knowledge.
We help our clients find industry experts who can provide their knowledge via interview or survey: we curate this knowledge in a market-leading software platform; and we help clients surface knowledge they already have through expansive knowledge management.
We’re looking for an Analytics Engineer to take ownership of our data platform within our small, high impact Data & Analytics team. You’ll work closely with the Head of Data & Analytics and a Commercial Data Analyst, playing a key role across the full data lifecycle – from ingestion and modelling through to self-serve analytics and stakeholder delivery. This is a hands-on, high-ownership role. You’ll own our dbt environment and warehouse, shape how data is modelled and used across the business, and help move us towards a scalable, self-serve analytics culture. You’ll also have the opportunity to explore how AI and modern tooling can improve both internal workflows and how stakeholders interact with data.
Our data stack:
- PostgreSQL data warehouse (AWS-hosted)
- AWS DMS & Fivetran (data ingestion)
- dbt Cloud (Enterprise)
- Tableau Server
Key duties in this role will include:
- Own and scale the data platform:
- Own the day-to-day health and performance of our dbt Cloud environment and PostgreSQL warehouse
- Design, build, and maintain scalable, analytics-ready data models using best practices (testing, version control, CI/CD)
- Improve data reliability, performance, and structure as we scale
- Drive data quality and governance across the business
- Build a self-serve data culture:
- Own and develop our data dictionary, making it a genuinely useful tool for the business
- Define and standardise key metrics, ensuring a clear single source of truth
- Enable non-technical teams to confidently use data through clear documentation and guidance
- Partner with the business:
- Work closely with Product, Client Services, and Finance to deliver high-quality datasets and dashboards
- Translate business questions into well-structured data models and metrics
- Replace manual, Excel-heavy reporting with scalable, automated pipelines
- Support product analytics by improving tracking, modelling, and insight generation
- Improve tooling & ways of working:
- Collaborate with Engineering to enhance data pipelines and architecture
- Continuously improve how we build, test, and deploy data models
- Explore and apply AI tools and automation to improve productivity and the analytics experience
Why this role?
- High ownership: you’ll shape the data platform, not just contribute to it
- Real impact: your work will directly influence decision-making across the business
- Early data team: opportunity to define standards, tooling, and best practices
- Modern stack: dbt, cloud warehouse, and a strong foundation to build on
- Room to innovate: especially around AI and automation
Strong SQL skills and experience working with dbt. Experience building and maintaining analytics-ready data models in a warehouse environment. Experience with a BI tool (Tableau or similar). A strong focus on data quality, testing, and reliability. Experience owning or contributing to data infrastructure and tooling. Clear, confident communication with both technical and non-technical audiences. Self-motivated and accountable, you take ownership and follow through.
Tenure Gifts – Vouchers, extra holiday and sabbaticals for each year of employment. Health insurance through Vitality. Enjoy the flexibility of working remotely for up to 20 days each year, allowing you to tailor your work environment to your needs and embrace a change of scenery. Employee Assistance Programme – Access to a health and wellbeing service that offers personalised advice and support from specialist teams. Enhanced Maternity & Paternity pay. Annual Leave – 25 days +
Analytics Engineer (Data Platform) in London employer: proSapient
At Sapient, we pride ourselves on being an excellent employer by fostering a culture of high ownership and innovation within our Data & Analytics team. Our Analytics Engineer role offers the chance to shape our data platform while enjoying benefits such as generous annual leave, health insurance, and flexible remote working options. We are committed to employee growth, providing opportunities to explore modern tools and AI, ensuring that your contributions have a real impact on decision-making across the business.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer (Data Platform) in London
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We think you need these skills to ace Analytics Engineer (Data Platform) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at proSapient, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at proSapient. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at proSapient
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at proSapient!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
