At a Glance
- Tasks: Own metrics, build dashboards, and analyse high-volume operational data.
- Company: Fast-scaling logistics-tech platform with a data-driven approach.
- Benefits: Competitive salary, equity bonuses, private health cover, and flexible working.
- Other info: Dynamic environment with fast hiring process and excellent career growth.
- Why this job: Make a real impact by shaping operations through data analysis.
- Qualifications: Strong SQL skills and experience with messy operational data required.
The predicted salary is between 60000 - 130000 £ per year.
They're a fast-scaling logistics-tech platform, backed by one of Europe's largest logistics Series A rounds, growing significantly faster than the typical venture-backed startup. Data sits at the centre of how they operate: every item they handle generates thousands of data points, and the team that works with that data directly shapes how the network runs. They're a centralised data function of engineers, analysts and data scientists, with analysts embedded into operational squads across the business.
Hiring across several squads spanning logistics operations, commercial, finance and network forecasting, at levels from Data Analyst through to Lead Data Analyst. Regardless of level or squad, the shape of the work is the same: you sit inside a specific part of the business, own the analytics that make its performance measurable, and work directly with the people making decisions on the back of it. This isn't reporting from a distance. You'll be close enough to the operation to understand why the numbers move, not just that they moved.
What you'll do:
- Own the metrics and KPIs for your squad, and build the monitoring and dashboards behind them.
- Investigate performance issues and cost drivers to root cause, and quantify the business impact.
- Translate messy, high-volume operational data into analysis that stakeholders trust and act on.
- Maintain and extend the dbt models and data pipelines underpinning your squad's reporting.
- Work cross-functionally with operations, engineering, commercial and finance to trace issues back to source (Lead level).
- Line manage a small team of analysts, setting direction and coaching alongside your own hands-on analysis.
What we're looking for:
- Strong SQL, non-negotiable at every level.
- Experience with dbt and a cloud data warehouse (BigQuery or similar).
- Comfortable with messy, high-volume operational data rather than clean, pre-modelled datasets.
- A track record of turning analysis into a decision someone actually acted on, not just insight for its own sake.
- Clear communicator, able to explain findings to non-technical stakeholders.
- 3+ years' experience for Data Analyst level, 5+ for Senior and Lead.
- Industry background is genuinely secondary. Logistics, delivery, marketplace or another high-volume operational business is a plus, not a requirement (Lead level).
- Prior experience managing or mentoring analysts.
Compensation, benefits and ways of working:
- Base salary £60k–£130k+ depending on level and squad, with an equity-heavy comp structure and annual top-ups.
- Performance-linked equity bonus on top of base equity (up to 100%).
- Private health and dental cover.
- 25 days holiday plus enhanced parental leave.
- Hardware of your choice.
- Gym subsidy, cycle-to-work scheme, Friday office lunch, and covered transport and dinner for late nights.
- 4 days on-site, 1 day remote.
- Fast-moving hiring process: typically SQL live coding, a case study, and a decision within 48 hours of final interview.
If this sounds like your kind of problem to solve, get in touch and I'll point you at the right squad and level for your background.
Senior Data Analyst employer: Wave Group
Wave Group is an excellent employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. Employees benefit from competitive salaries, flexible hybrid working arrangements, and opportunities for professional growth while contributing to impactful projects that modernize policing through advanced AI solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Analyst
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Wave Group!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Analyst at Wave Group.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Wave Group.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Analyst at Wave Group, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Data Analyst
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 Wave Group, 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 Wave Group. 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 Wave Group
✨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!
✨Showcase Your Projects
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 Wave Group!
✨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.
