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- Tasks: Build and maintain scalable data pipelines using Airflow and dbt on AWS.
- Company: Fast-growing B2B financial services business based in London.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Join a dynamic team focused on data quality and governance.
- Why this job: Tackle interesting engineering challenges and modernise data infrastructure with cutting-edge technology.
- Qualifications: Proven experience in data engineering, strong Python and SQL skills required.
The predicted salary is between 75000 - 105000 £ per year.
I'm working with a fast-growing B2B finserv business based in London, and they're looking for a Senior Data Engineer to join their data platform team. This is a genuinely interesting engineering problem — they're sitting on a large volume of valuable financial data and are in the process of modernising their data infrastructure, moving away from legacy systems towards a scalable, cloud-native stack.
What you'll be working on:
- Building and maintaining scalable data pipelines using Airflow and dbt
- Working across AWS cloud infrastructure
- Collaborating with analysts and data scientists to deliver reliable, clean data for downstream reporting and business decision-making
- Contributing to data quality, governance, and best practices across the team
What they're looking for:
- Proven data engineering experience with strong Python and SQL
- Hands-on with Airflow and/or dbt
- Solid AWS experience
- Experience with ETL pipeline development at scale
- Comfortable working in Agile/Scrum environments
- Full right to work in the UK — no sponsorship available for this role
If this sounds interesting or you know someone who might be a fit, drop me a message or apply below.
Senior Data Engineer (AWS) employer: Kinvr Digital
Contact Detail:
Kinvr Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer (AWS)
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, especially those who work at companies you're interested in. A friendly chat can sometimes lead to job opportunities that aren't even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those involving AWS, Python, and data pipelines. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by practising common technical questions related to data engineering. Brush up on your knowledge of Airflow, dbt, and AWS. The more confident you are, the better you'll perform!
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find roles that match your skills and interests. Plus, it shows you're serious about joining our team!
We think you need these skills to ace Senior Data Engineer (AWS)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with AWS, Python, and SQL. We want to see how your skills align with the role, so don’t be shy about showcasing your relevant projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this role and how your background makes you a perfect fit for our data platform team. Keep it engaging and personal!
Showcase Your Problem-Solving Skills: We love engineers who can tackle complex problems. In your application, mention specific challenges you've faced in data engineering and how you overcame them. This will help us see your thought process and approach!
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Kinvr Digital
✨Know Your Tech Stack
Make sure you brush up on your AWS knowledge, especially around data engineering tools like Airflow and dbt. Be ready to discuss how you've used these technologies in past projects, as this will show your hands-on experience.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific engineering challenges you've faced, particularly in modernising data infrastructures. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your contributions.
✨Collaboration is Key
Since you'll be working closely with analysts and data scientists, think of examples where you've successfully collaborated with cross-functional teams. Emphasise your communication skills and how you ensure data quality and governance.
✨Be Agile Ready
Familiarise yourself with Agile/Scrum methodologies if you haven't already. Be prepared to discuss how you've adapted to changing requirements in previous roles and how you prioritise tasks in a fast-paced environment.