At a Glance
- Tasks: Build and integrate datasets into client workflows to enhance a scalable SaaS product.
- Company: Join an early-stage B2B SaaS business on an exciting growth journey.
- Benefits: Competitive salary, benefits package, annual bonus, and stock options.
- Other info: Hybrid role with great opportunities for career growth in a dynamic environment.
- Why this job: Transform client operations with innovative data solutions and cutting-edge technology.
- Qualifications: 3-4 years in data engineering and client-facing experience required.
The predicted salary is between 36000 - 60000 £ per year.
With a new year comes new opportunities and I am delighted to be partnered with an early stage B2B SaaS business as they continue their impressive growth journey from 2025. Such is their success that they are now looking to hire a number of people across the business, one such hire is an exciting opportunity for a Data Engineer to become a part of the growing team to help transform how their clients and customers work.
Overview
This role is focused on building out the core product by integrating new datasets into client workflows, with a focus on identifying the commonalities between different client installations to create a scalable SaaS product.
Responsibilities
- This role primarily covers building out the core product by integrating new datasets into client workflows, with a focus on identifying the commonalities between different client installations to create a scalable SaaS product.
- Work with clients to understand their needs, priorities, context and key outcomes.
- Ensure client implementations work successfully.
- Solve customers’ most difficult business challenges by adapting and deploying platform to meet their needs.
- Design and implement scalable generative AI workflows using platforms like Palantir AIP.
- Execute complex data integration using PySpark and other distributed technologies.
- Collaborate with internal teams and stakeholders as required.
Key Skills
- 3-4 years of solid experience in data engineering.
- Client facing experience.
- Experience working in a start-up / early stage organisation.
- The ability to translate complex and sometimes ambiguous business requirements into clean and maintainable data pipelines.
- Excellent knowledge of PySpark, Python and SQL fundamentals.
- The ability to get to grips with new technologies quickly.
Nice to Have
- Experience in dashboarding tools, Typescript and API development.
- Familiarity with Airflow, DBT, Databricks.
- Experience working with Enterprise Resource Planning (e.g. Oracle, SAP) and CRM systems.
Other Details
- This role is a Hybrid role with the expectation of working in the London office 3 days per week.
- My client cannot offer sponsorship at this time.
- Compensation: Competitive salary - plus benefits package, annual bonus and stock options.
If you’re excited by everything you’ve read above and have the experience to match, please get in touch as we would love to tell you more! Email – colin@vividrock.co.uk
Finally… We are expecting an exceptionally high volume of applications; sadly, we are unlikely to be able to respond to each individual applicant. Thanks for being patient and we’ll be sure to get in touch if we’re keen to take your application further. Vivid Rock is passionate about championing diversity and challenging unconscious bias and treats each application carefully. Thank you for your interest and please follow our page for further updates.
Data Engineer employer: Vivid Rock
Join an innovative early-stage B2B SaaS company that is committed to transforming client workflows through cutting-edge data engineering. With a vibrant work culture that fosters collaboration and creativity, employees are encouraged to grow their skills and advance their careers while enjoying competitive salaries, a comprehensive benefits package, and the flexibility of a hybrid working model in the heart of London. This is an exciting opportunity to be part of a dynamic team that values diversity and is dedicated to solving complex business challenges.
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 Vivid Rock!
✨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 Vivid Rock.
✨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 Vivid Rock.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Vivid Rock, 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 Vivid Rock, 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 Vivid Rock. 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 Vivid Rock
✨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 Vivid Rock!
✨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.
