At a Glance
- Tasks: Design and maintain data pipelines to drive analytics and business decisions.
- Company: Join Arrive, a global leader in urban mobility solutions.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment focused on innovation and community improvement.
- Why this job: Make a real impact on urban mobility and help shape smarter cities.
- Qualifications: 5+ years in Analytics Engineering with strong SQL and Python skills.
The predicted salary is between 63000 - 77000 £ per year.
We’ve signed up to an ambitious journey. Join us!
As Arrive, we guide customers and communities towards brighter futures and more livable cities, it isn’t a challenge just anyone could take on.
Luckily, we have something to help us make it happen.
Our people and our values.
We Arrive Curious, Focused and Together.
Just as our entire brand is inspired by the North Star, the shining light leading travelers to their destinations since time began, our values guide us.
They help us be at our best.
For our customers.
For the cities and communities we serve.
For ourselves.
As a global team, we are transforming urban mobility.
Let’s grow better, together.
An exciting hybrid working opportunity has arisen for a talented and driven Senior Analytics Engineer within our Your Parking Space business in London (SE1).
The Role
As a Senior Analytics Engineer, you'll play a critical role in shaping and driving the data strategy that powers our analytics, products and commercial decision-making.
Beyond building and maintaining robust data infrastructure, you'll act as the bridge between technical delivery and business outcomes, ensuring our data capabilities are aligned with commercial priorities and that stakeholders across the business can confidently rely on data to inform their decisions.
This is a hands‑on senior‑level role where you'll own data pipelines end to end, from ingesting raw data from multiple sources to transforming and delivering high‑quality, analytics‑ready datasets.
You'll also contribute to the data roadmap, working closely with business stakeholders to understand their needs, prioritise work that delivers the most value, and clearly communicate progress and technical trade‑offs.
- How To Make An Impact
- Design, build and maintain end‑to‑end data pipelines, including ingestion, transformation and delivery of data to analytics and reporting layers.
- Own and improve existing data pipelines, ensuring high reliability, performance and scalability.
- Set up new pipelines for internal and external data sources, selecting appropriate tools and patterns.
- Monitor data quality, accuracy and pipeline health, proactively identifying and resolving issues to ensure high levels of data trust and uptime.
- Implement analytics engineering best practices, including testing, documentation, version control and observability.
- Continuously improve data architecture, modelling and workflows to support growing data volumes and evolving business needs.
- Partner with commercial, product and operational stakeholders to understand their data needs and translate business questions into well‑scoped analytics engineering solutions.
- Help shape and prioritise the analytics engineering backlog, balancing technical improvements with high‑impact business requirements.
- Define and communicate the data scope for new initiatives, providing clear assessments of feasibility, effort and expected value.
- Your background
- 5+ years' experience in Analytics Engineering, BI Engineering or Data Engineering, with experience operating at Senior or Lead level.
- Strong SQL and Python skills, including hands‑on experience building and optimising production ELT/ETL pipelines and complex data transformations.
- Experience designing, building and maintaining end‑to‑end data pipelines using modern data stack technologies.
- Strong understanding of data modelling and analytics‑ready datasets, with a focus on data quality, reliability and scalability.
- Experience partnering with commercial, product or operational stakeholders, translating business requirements into effective data solutions and helping prioritise work based on business value.
- Strong communication skills, with the ability to take ownership, influence priorities and explain technical concepts and trade‑offs to both technical and non‑technical stakeholder
This role is hybrid role based in London (SE1), 3 days in the office per week, 2 days from home.
About Arrive
Arrive, including brands like Easy Park, Flowbird, Ring Go, Park Mobile and Parkopedia, is a leading global mobility platform.
Present in over 90 countries and 20,000 cities, the company helps people and decision‑makers make smarter decisions about urban mobility and ease the experience of travel worldwide.
Arrive delivers a unique combination of the core ingredients to make cities more livable: from smart payments and optimized car parks to data‑driven traffic reduction and support for reinvestment in public transport and green space.
It’s about more than function, it’s about saving time and simplifying the experience of travel for everyone.
Travel is more than a journey, it’s how you Arrive.
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Senior Analytics Engineer in London employer: Arrive
At Arrive, we pride ourselves on being an exceptional employer that fosters a culture of curiosity, collaboration, and innovation. Our hybrid working model in London allows for flexibility while you contribute to meaningful projects that shape urban mobility. With a strong focus on employee growth and development, we provide opportunities for our team members to thrive and make a real impact in the communities we serve.
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We think you need these skills to ace Senior Analytics Engineer 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!
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Craft a Tailored Cover Letter:For a full-time role at Arrive, 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 Arrive. 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 Arrive
✨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 Arrive!
✨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.
