Staff Software Engineer - Streaming
Checkout.com is hiring a Staff Software Engineer - Streaming in London, United Kingdom. Level rates it ; you can apply on Checkout.com.
Level's read on this job
- Works on AI ●●●○ is how Level rates this software engineer role: the daily work is on AI products, without building the model.
- The posting mentions Kafka, Flink and data engineering among its tools and skills.
- Level classes it as a senior role.
- Checkout.com has 171 other open roles on Level.
AI in this role
Build and scale a reliable event streaming platform supporting AI-powered data applications and large-scale data processing.
Company Description
We’re Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.
We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.
Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.
If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.
With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.
Job Description
About the role
Checkout.com is looking for an ambitious Staff Data Engineer to join our Data and AI Platform Team. Our team’s mission is to build a platform where you can create reliable, scalable, AI-powered streaming and batch data applications, and share data across Checkout.com to improve business performance.
The Data and AI Platform team is here to ensure internal stakeholders can easily collect, store, process and utilise data to build AI use cases and data products aiming to solve business problems. Our focus is on maximising the amount of time engineers spend on solving business problems and minimising time spent on technical details around implementation, deployment, and monitoring of their solutions.
We're building for scale. As such, much of what we design and implement today is the technology/infrastructure which will serve hundreds of teams and petabyte-level volumes of data.
Key Responsibilities
Work with stream processing technologies (Kafka and Flink) to build a continuously available large-scale event streaming platform
Leverage subject matter and technical expertise to provide leadership, mentoring, and strategic influence across the organisation whilst building strong relationships with engineers and engineering managers
Build tooling (modules/SDKs/DSLs) and associated documentation to foster the adoption of the streaming platform by enabling upstream teams and systems to easily publish data and deploy streaming applications
Implement all the necessary infrastructure to enable end users to build, host, monitor and deploy their own streaming applications
Provide consultancy across the technology organisation to drive the adoption of the platform and unlock event-driven use-cases
Participate, translate, run and execute the collection of requirements and architecture/design initiatives into action plans
Provide hands-on support for all event-based systems including incident triage and root cause analysis
About You
While experience with our specific tech stack is a plus, we welcome candidates with a strong background in data systems who are eager to learn. The core remit of this role is to own and scale our event streaming capability, not to serve as a general DevOps or infrastructure engineer.
Strong presentation and communication skills with a proven track record of influencing engineering organisations
Strong engineering background with a track record of implementing and owning event streaming platforms
Hands-on experience working with stream technologies, ideally Kafka
Experience designing and implementing stream processing applications with Flink
Experience working with cloud-based technologies such as AWS (MSK, S3, Lambda, ECS, SNS)
Experience with Kubernetes (either self-hosted or on the cloud)
Experience with SQL databases
Experience working with Docker, container deployment and management
Experience describing infrastructure as code (Terraform or similar) as well as designing and implementing CI/CD pipelines
Excellent programming skills with at least one of Java or Python
Additional Information
Bring all of you to work
We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.
Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.
We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.
It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
Life at Checkout.com
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.
For a closer look at daily life at Checkout.com, follow us on LinkedIn and Instagram
How we rate this
Staff Software Engineer - Streaming at Checkout.com rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in software engineer jobs at this level
Pay
- Typical software engineer job
- $167k
This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs
Questions you could be asked
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where stream processing was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- What's a project where you used Kafka hands-on?
- Walk me through how you've used Flink in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Data Engineering, Stream Processing, Distributed Systems, Kafka, and Flink. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
Get your CV reviewed before you apply
Send your CV and the role you want next. Your review arrives by email within 3 days.
Get new software engineer jobs (Works on AI ●●●○ or higher) by email
One email a week, each job rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free.
Similar roles
Software Engineering roles that work on AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step