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: Collaborative Agile/Scrum environment with a focus on data quality and governance.
- Why this job: Tackle interesting engineering challenges while modernising data infrastructure.
- Qualifications: Proven experience in data engineering with strong Python and SQL skills.
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.
- 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
Proven data engineering experience with strong Python and SQL.
Solid AWS experience.
Comfortable working in Agile/Scrum environments.
AWS Data Engineer - Hybrid/Remote employer: Kinvr Digital
Contact Detail:
Kinvr Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AWS Data Engineer - Hybrid/Remote
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at the company you're eyeing. A friendly chat can sometimes lead to job opportunities that aren't even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS, Python, and SQL. This gives you a chance to demonstrate your expertise and makes you stand out from the crowd.
✨Tip Number 3
Prepare for the interview like it’s the final boss level! Research the company’s data infrastructure and be ready to discuss how you can contribute to their modernisation efforts. Tailor your answers to reflect their needs and challenges.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step to engage with us directly.
We think you need these skills to ace AWS Data Engineer - Hybrid/Remote
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 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 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: Since we’re tackling interesting engineering problems, share examples of how you've approached challenges in the past. Highlight your experience with building scalable data pipelines and working in Agile/Scrum environments.
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 AWS Inside Out
Make sure you brush up on your AWS knowledge before the interview. Familiarise yourself with the specific services mentioned in the job description, like S3, Redshift, and Lambda. Being able to discuss how you've used these tools in past projects will show that you're not just a theoretical expert but someone who can apply their knowledge practically.
✨Showcase Your Data Pipeline Skills
Prepare to talk about your experience with building and maintaining data pipelines, especially using Airflow and dbt. Have examples ready that demonstrate how you've tackled challenges in data engineering and improved data flow efficiency. This will highlight your hands-on experience and problem-solving abilities.
✨Collaboration is Key
Since the role involves working closely with analysts and data scientists, be ready to discuss how you've collaborated in the past. Share specific instances where your teamwork led to successful outcomes, particularly in delivering clean and reliable data for decision-making. This shows you’re a team player and understand the importance of cross-functional collaboration.
✨Embrace Agile Methodologies
Familiarise yourself with Agile and Scrum practices if you haven't already. Be prepared to discuss how you've worked in Agile environments before, including any roles you've played in sprints or stand-ups. This will demonstrate that you can adapt to their workflow and contribute effectively from day one.