At a Glance
- Tasks: Own and evolve data foundations, ensuring clean pipelines and reliable datasets.
- Company: Join Plain, a forward-thinking company redefining B2B customer support with AI.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Dynamic startup culture with a focus on innovation and collaboration.
- Why this job: Make a real impact by shaping data strategies that enhance customer relationships.
- Qualifications: Experience in building analytics stacks and strong SQL skills required.
The predicted salary is between 60000 - 80000 £ per year.
Who is Plain? Plain is redefining customer support for the next generation of B2B companies. We’re building the fastest, most powerful platform to help companies move beyond reactive support and build true customer relationships. Some of the world’s most forward-thinking companies trust Plain to unify all customer interactions, enable faster team collaboration, and supercharge their workflows with AI.
B2B customer support is undergoing a seismic shift. AI is transforming the way companies engage with customers, shifting support from a siloed function to a company-wide effort across Slack, Discord, and any other channel you talk to customers in. The old way - slow, manual, and disconnected - no longer works.
The role involves hiring a Founding Data Engineer to own and evolve Plain’s data foundations: the warehouse, core models, and the “customer/account spine” that Product, GTM, Support, and Engineering rely on to make decisions and build great experiences. This is a hands-on role. You’ll work across our data stack and partner closely with engineering teams to keep our event taxonomy, pipelines, and metrics clean as we scale.
What you'll do:
- Rebuild our data warehouse: own the architecture, schemas, and core datasets with clean pipelines and a unified event taxonomy established with Engineering.
- Deliver trusted, reusable data products: foundational datasets that power analytics, reporting, in-app features, and AI, anchored on a joinable customer/account spine across product, billing, and CS context.
- Stand up data observability: quality checks, freshness, lineage, schema drift, and incident response, so the business can trust what it sees.
- Own in-app reporting: ship the analytics features that help support leaders turn their data into better decisions.
- Enable self-serve: evolve our data layer, dashboards, and documentation so every team can run their own analysis without a ticket.
- Lay the retrieval layer behind our AI agent's customer context.
- Partner across the company: work with GTM, CX, Product, and Engineering to translate questions into scalable models and datasets.
This is a great fit if you…
- Have built modern analytics stacks end-to-end (warehouse, transformations, semantic layer, governance) from zero, ideally more than once.
- Are strong with SQL, BigQuery, and dbt/Dataform.
- Have experience building user-facing analytics or AI retrieval layers using real-time data platforms (e.g., Tinybird, ClickHouse).
- Care about data quality, trust, and reusability as much as shipping speed.
- Take initiative and measure your work by end-user impact, not elegant abstractions.
- Communicate clearly and build alignment without heavy process.
This won't be the right role if you…
- Are uncomfortable with ambiguity or greenfield work. We're early and moving fast.
- Prefer exploratory analysis over engineering reliable datasets and systems.
- Want to manage a team right now. This is an IC role.
Founding Data Engineer in London employer: Plain
At Plain, we pride ourselves on fostering a dynamic and innovative work culture that empowers our employees to take ownership of their projects and drive meaningful change in the B2B landscape. Located in a vibrant tech hub, we offer competitive benefits, continuous learning opportunities, and a collaborative environment where creativity thrives, making us an excellent employer for those looking to make a significant impact in product design.
StudySmarter Expert Advice🤫
We think this is how you could land Founding Data Engineer in London
✨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 Plain!
✨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 Founding Data Engineer at Plain.
✨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 Plain.
✨Apply Directly through Our Website
When you find a suitable opening like Founding Data Engineer at Plain, 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 Founding Data 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!
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 Plain, 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 Plain. 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 Plain
✨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 Plain!
✨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.
