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- Tasks: Design scalable data solutions and integrate datasets into client workflows.
- Company: B2B SaaS business in Greater London with a focus on innovation.
- Benefits: Competitive salary, annual bonus, stock options, and hybrid work flexibility.
- Why this job: Join a dynamic team and shape the future of AI-powered SaaS products.
- Qualifications: 3-4 years of data engineering experience and proficiency in PySpark and Python.
- Other info: Exciting opportunity for career growth in a collaborative environment.
The predicted salary is between 36000 - 60000 £ per year.
A B2B SaaS business in Greater London is seeking a Data Engineer to help build their core product by integrating datasets into client workflows. The role involves designing scalable data solutions, working closely with clients, and utilizing tools such as PySpark and Python.
Candidates should have 3-4 years of data engineering experience and be comfortable in a hybrid work environment. Competitive salary and benefits are offered, including an annual bonus and stock options.
Data Engineer: Build Scalable AI-Powered SaaS Pipelines employer: Vivid Rock
Contact Detail:
Vivid Rock Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer: Build Scalable AI-Powered SaaS Pipelines
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow data engineers. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving PySpark and Python. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and scenarios. Practice explaining your thought process when designing scalable solutions, as this is key for the role.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be perfect for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Data Engineer: Build Scalable AI-Powered SaaS Pipelines
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with data engineering, especially using tools like PySpark and Python. We want to see how your skills align with what we’re looking for, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Tell us why you’re excited about the role and how you can contribute to our team. We love hearing about your passion for building scalable solutions and working closely with clients.
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled challenges in previous roles. We’re keen to see your thought process and how you approach designing data solutions that integrate seamlessly into workflows.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Vivid Rock
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like PySpark and Python. Brush up on your coding skills and be ready to discuss how you've used these technologies in past projects.
✨Understand the Business
Research the B2B SaaS industry and the specific company you're interviewing with. Knowing their core product and how data integration plays a role will show that you’re genuinely interested and can contribute effectively.
✨Prepare for Scenario Questions
Expect questions that ask you to solve real-world problems related to data engineering. Think about past experiences where you designed scalable solutions and be ready to explain your thought process clearly.
✨Show Your Collaboration Skills
Since the role involves working closely with clients, be prepared to discuss how you’ve successfully collaborated with others in previous roles. Highlight any experience you have in a hybrid work environment to demonstrate your adaptability.
