At a Glance
- Tasks: Monitor performance, build dashboards, and provide insights for a cutting-edge AI media platform.
- Company: Join Runware, a fast-growing AI media-creation platform with a remote-first culture.
- Benefits: Enjoy flexible hours, generous paid time off, and meaningful stock options.
- Other info: Work autonomously while collaborating with diverse teams and enjoy company retreats twice a year.
- Why this job: Make a real impact by enhancing platform performance and reliability in a dynamic environment.
- Qualifications: Strong data analytics experience and familiarity with monitoring frameworks required.
The predicted salary is between 36000 - 60000 € per year.
About Runware
Runware is building a high-performance, full-stack AI media-creation platform — empowering developers and companies to generate any type of media instantly. As we scale fast and integrate increasingly complex models, we need stronger visibility, analytics, and monitoring across the whole platform stack.
Mission
Your main goal is to give Runware full visibility over:
- End-to-end inference performance
- Integration usage and model activity
- Errors, delays, bottlenecks, regressions
- Internal and client-facing analytics dashboards
- Health and performance of production pipelines
You will provide the data insights that allow engineering, ML, backend, DevOps, and leadership to make informed decisions — and to continuously improve performance and reliability.
What You Will Do
Performance Monitoring & Benchmarking
- Build and maintain E2E inference time tracking (global and per-model).
- Monitor how implementation changes impact total request latency.
- Detect regressions introduced by suboptimal code paths.
- Provide automated alerts & historical trends.
Usage & Analytics Reporting
- Build dashboards for internal use (engineering, product, leadership).
- Provide client-facing usage dashboards (requests, errors, success rate, performance).
- Support clients who need visibility to debug their integrations.
- Track model-level usage, API endpoints usage, adoption metrics, etc.
Platform Observability
- Implement metrics, logs, and traces that help the entire platform scale smoothly.
- Work closely with DevOps & backend teams to improve system observability.
- Provide insights that guide infra decisions (GPU allocation, autoscaling, caching, batching, etc.).
Data Infrastructure Ownership
- Select and maintain tooling (e.g., Prometheus/Grafana, Datadog, OpenTelemetry, ELK, BigQuery, etc.).
- Ensure data pipelines are reliable, accessible, and always up-to-date.
- Build simple, easy-to-read dashboards for both technical and non-technical teams.
Requirements
Must-Have
- Strong experience with data analytics, observability, or monitoring
- Hands-on with metrics/logging/tracing frameworks (Prometheus, Grafana, Datadog, New Relic, etc.)
- Good understanding of backend systems and distributed architectures
- Ability to turn raw metrics into actionable insights
- Experience building dashboards for internal and external stakeholders
- Familiarity with AI model monitoring (latency, throughput, error codes, GPU utilization)
Nice-to-Have
- Experience with AI/ML infrastructure, inference pipelines, GPUs
- Understanding of Python APIs, FastAPI, or Node environments
- Experience working with high-throughput real-time systems
- Startup or scale-up experience
What You Bring
- A problem-solver mindset
- Proactivity — you like digging into the data and flagging problems before anyone else sees them
- Ability to work with ML, backend, DevOps, and product teams
- Comfort with autonomous ownership
Benefits
- We're a remote-first collective, meeting in person twice a year to plan, brainstorm, celebrate wins, and enjoy some face-to-face time.
- We have core hours for cooperative working and calls, but outside of that your calendar is yours. Work the hours that let you perform at your peak while also building a healthy life.
- Our release cycles are fast and intense, but they're followed by real downtime. After big pushes we expect the team to unplug, recharge, and come back ready & stronger than ever for the next leap.
- Generous paid time off - vacation, sick days, public holidays
- Meaningful stock options - share in the upside you create
- Remote-first setup - work from home anywhere we can employ you
- Flexible hours - own your schedule outside core collaboration blocks
- Family leave - paid maternity, paternity, and caregiver time
- Company retreats - twice-yearly gatherings in inspiring locations
Please note: We are unable to offer visa sponsorship in the UK at this time. Candidates must have existing right to work in the UK.
Platform Data Analyst in London employer: Runware
Runware is an exceptional employer that champions a remote-first work culture, allowing you to thrive in your role as a Platform Data Analyst while enjoying the flexibility of working from anywhere. With generous paid time off, meaningful stock options, and opportunities for professional growth, you will be part of a dynamic team that values collaboration and innovation, meeting twice a year to celebrate achievements and strategise for the future.
StudySmarter Expert Advice🤫
We think this is how you could land Platform Data Analyst in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or a personal project that highlights your data analytics prowess. This is a great way to demonstrate your abilities beyond just a CV.
✨Tip Number 3
Prepare for interviews by practising common questions related to data analytics and observability. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Runware.
We think you need these skills to ace Platform Data Analyst in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Platform Data Analyst role. Highlight your experience with data analytics, observability, and any relevant tools like Prometheus or Grafana. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data analytics and how your background makes you a perfect fit for Runware. Don’t forget to mention any specific projects or achievements that showcase your problem-solving mindset.
Showcase Your Technical Skills:In your application, be sure to highlight your hands-on experience with metrics, logging, and tracing frameworks. If you've built dashboards for internal or external stakeholders, let us know! We love seeing how you turn raw data into actionable insights.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative to connect with us directly!
How to prepare for a job interview at Runware
✨Know Your Data Tools
Make sure you’re familiar with the data analytics and monitoring tools mentioned in the job description, like Prometheus, Grafana, and Datadog. Be ready to discuss your hands-on experience with these tools and how you've used them to derive actionable insights.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex data issues in the past. Highlight your proactive approach to identifying problems before they escalate, especially in high-throughput real-time systems. This will demonstrate your problem-solver mindset that Runware values.
✨Understand the Bigger Picture
Familiarise yourself with how data insights impact engineering, ML, and DevOps teams. Be prepared to discuss how your work can influence decisions on GPU allocation, autoscaling, and other infrastructure choices. This shows you’re not just focused on data but also on its implications.
✨Prepare for Technical Questions
Expect technical questions related to backend systems and distributed architectures. Brush up on your knowledge of AI model monitoring metrics like latency and throughput. Being able to speak confidently about these topics will set you apart from other candidates.
