At a Glance
- Tasks: Build innovative data products and maintain robust data pipelines in a fast-paced environment.
- Company: Join a dynamic team transforming a services-led business into an AI-native powerhouse.
- Benefits: Enjoy real ownership, equity, and the chance to make an immediate impact.
- Other info: Collaborative culture with significant room for growth and learning.
- Why this job: Be part of a small team where your work ships fast and is used right away.
- Qualifications: Experience in building production data pipelines and strong engineering fundamentals.
The predicted salary is between 60000 - 80000 £ per year.
Join a small team building a 0-1 product, bootstrapped, but backed by the resources of an established, profitable parent business. Your work ships fast and gets used immediately by the team you sit alongside, so you test, iterate, and feel the impact within days rather than quarters. There's a capable go-to-market function ready to take what you build to market, and significant room to scale quickly. You get real ownership, real equity, and a genuine voice in the roadmap, in an AI-native team that treats the latest tools as a daily multiplier rather than a novelty.
About us
We're transforming a services-led business into an AI-native one, with technology genuinely at the core rather than bolted on. A small, high-leverage team has been put in place to build this out. The team is growing as the product becomes revenue-generating. We test, validate, and iterate quickly.
What we're building
- An internal data platform: a centralised data lake, copilot, and dashboard product that stitches together operational data, historical records, and enrichment from first-, second-, and third-party sources.
- Internal tooling that removes manual admin from day-to-day operations.
- An external product: an AI-driven matching platform already live with paying customers.
The role
This hire sits between our product engineers and our data engineering foundation. You'll move fluidly between the two: one week shaping an internal dashboard or workflow, the next hardening a data pipeline or evolving the schema everything downstream depends on. Expect roughly a 50/50 split between product-surface work and data work. On the product side you'll build interfaces, copilot-style workflows, and AI-assisted experiences. On the data side you'll own pipelines, data quality, and modelling, the foundation layer every output relies on. A degraded pipeline is a commercial issue before it's a technical one, and you'll treat it that way.
Above all, this is an engineering hire. We're optimising for someone who owns work end-to-end and needs light oversight, not hand-holding. You ship to 100% when it matters and a deliberate 80% when speed matters more, and you know the difference.
What you'll do
- Build product surfaces: interfaces, dashboards, and AI-assisted workflows that turn structured data into useful actions for internal and external users.
- Own the data foundation: build and maintain pipelines ingesting data from multiple operational systems, monitored, documented, and alerting on failure before it affects downstream outputs.
- Guard data quality: completeness checks, anomaly detection, freshness monitoring, surfaced before they affect anything built on top.
- Model the data: design and evolve the schema so it serves both structured reporting and LLM reasoning access.
- Build integrations and enrichment: maintain system integrations and evaluate third-party enrichment sources.
- Build trustworthy AI experiences: guardrails, transparency, and user control, so AI features feel reliable, not gimmicky.
- Ship fast and learn: deploy regularly, instrument adoption, iterate on real usage.
About you
- Trusted to deliver: you own work end to end with light oversight.
- Strong engineer, language-agnostic: real craft and good fundamentals. We don't have a hard TypeScript/React requirement, a good engineer is a good engineer, what matters is genuine ability to move across stacks. Strong production Python and SQL are the core.
- Genuine data depth: you've built and maintained production data pipelines at scale, not just designed them. Cloud data infrastructure experience preferred.
- The shape we're looking for: ideally a genuine multi-year product or front-end chapter before moving into data, someone who's built real product and then developed data depth. Roughly 4-6 years building and shipping software.
- AI-native, the critical signal: you use AI and coding agents as a force multiplier in real production settings, not a substitute for thinking. A plus if you build with AI in your own time.
- Comfortable with ambiguity: you translate business outcomes into technical work without needing a complete spec.
- Collaborative and clear: you document clearly and communicate well across the team.
- Bias toward simple systems: simple, maintainable systems over clever ones.
- Stack: Python, SQL, PostgreSQL, Supabase, Google Cloud, and React/Next.js, Node.js/TypeScript on the product side.
Data Engineer 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 Data Engineer
✨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 Data Engineer 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 Data Engineer 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 Data Engineer
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.
