At a Glance
- Tasks: Shape the data platform architecture and build high-throughput, reliable pipelines.
- Company: Innovative company transforming how data is stored and delivered at scale.
- Benefits: Competitive salary, stock options, and a high-growth environment.
- Other info: Dynamic team environment focused on innovation and complex challenges.
- Why this job: Work on real data systems with significant impact and ownership.
- Qualifications: 8+ years in Data Engineering, strong coding skills, and experience with distributed systems.
The predicted salary is between 75600 - 92400 £ per year.
This is what happens when data becomes the product.
Not just something that sits behind it.
I'm working with a company building the data layer behind how modern software is stored, secured, and delivered at serious scale (tens of millions of requests daily, petabytes of data monthly)
They're hiring a Staff Data Engineer to help shape that platform.
What's actually going on
Every action in their system is tracked
- Packages moving through the platform
- User activity
- Security decisions
- Usage + billing data
That data feeds directly into the product.
Right now, the focus is
- Building CDC pipelines (track every change, nothing lost)
- Handling billions of events in near real-time
- Redesigning log/export systems at scale
- Laying foundations for AI-driven insights
It's part greenfield, part scaling what's already there.
What you'll do
- Shape the data platform architecture
- Build high-throughput, reliable pipelines
- Work across teams (security, product, billing)
- Solve system-level problems, not just tickets
- Raise the bar for how data is used
- The reality of the role
This isn't a clean, well-defined data problem.
If you need perfect specs before starting, this won't suit you.
- What they're looking for
- 8+ years of experience in Data Engineering or Distributed Systems
- Strong data engineering experience at scale
- Experience with distributed / event-driven systems
- AWS + modern tooling (Kafka/Kinesis, Postgres, etc.)
- Strong coding skills (Python/SQL)
More importantly
- You care how the data is actually used
- You think in systems, not pipelines
- You're comfortable working without a playbook
- Why it's worth a look
- Core product infrastructure, not reporting
- Real scale, real complexity
- Still plenty of greenfield work
- Data platform is central to what they build next
Small team, high ownership, engineering-led environment.
Competitive salary + stock (recently funded, high-growth environment).
Candidates must be based in Ireland or the UK, with the right to work (no sponsorship).
If you've worked on real data systems and want something with more depth, this is worth a chat.
Staff Data Engineer in Milton Keynes employer: nineDots.io
At nineDots.io, we pride ourselves on being an excellent employer that fosters a collaborative and innovative work culture. With a strong focus on employee growth, we offer continuous learning opportunities and the chance to work in a dynamic environment within the London Market insurance sector. Our hybrid working model not only promotes work-life balance but also allows you to thrive in both office and remote settings, making it a rewarding place to advance your career in IT security.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Data Engineer in Milton Keynes
✨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 nineDots.io!
✨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 Staff Data Engineer at nineDots.io.
✨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 nineDots.io.
✨Apply Directly through Our Website
When you find a suitable opening like Staff Data Engineer at nineDots.io, 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 Staff Data Engineer in Milton Keynes
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 nineDots.io, 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 nineDots.io. 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 nineDots.io
✨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 nineDots.io!
✨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.
