Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- 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 collaborative team focused on data quality and best practices.
- Why this job: Tackle interesting engineering challenges with valuable financial data in a modernised infrastructure.
- Qualifications: Proven experience in data engineering with Python, SQL, and AWS.
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) in London employer: Kinvr Digital
Contact Detail:
Kinvr Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer (AWS) in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, especially those who work with AWS or data engineering. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Airflow, dbt, and AWS. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering scenarios. Be ready to discuss how you've tackled challenges in building scalable data pipelines and ensuring data quality. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Senior Data Engineer (AWS) in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, SQL, and AWS. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the opportunity and how your background makes you a perfect fit for our data platform team. Keep it engaging and personal.
Showcase Your Problem-Solving Skills: In your application, mention specific challenges you've tackled in data engineering. We love seeing how you approach problems, especially those related to building scalable data pipelines or working with ETL processes.
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’s super easy!
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.
✨Get Agile Ready
Familiarise yourself with Agile/Scrum methodologies if you haven't already. Be prepared to discuss how you've adapted to Agile environments in the past and how it has influenced your approach to data engineering.