At a Glance
- Tasks: Build and scale cutting-edge analytics platforms for high-impact research.
- Company: Join a leading firm in quantitative finance with a focus on innovation.
- Benefits: Competitive pay, 35 days leave, healthcare, and monthly events.
- Other info: Inclusive culture with excellent work/life balance and growth opportunities.
- Why this job: Shape the future of finance with advanced technologies and collaborative teams.
- Qualifications: Experience with distributed systems, Python, and AWS technologies.
The predicted salary is between 60000 - 80000 £ per year.
We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity. From our London HQ, we unite world‑class researchers and engineers in an environment that values deep exploration and methodical execution.
As part of our engineering team, you’ll shape the platforms and tools that drive high‑impact research – designing systems that scale, accelerate discovery and support innovation across the firm. The role involves designing, building and operating large‑scale distributed platforms that power our research, trading and engineering teams across on‑premises and AWS environments, using technologies such as Spark, Trino, Kafka, ClickHouse and Airflow.
Key Responsibilities- Building, operating and scaling distributed analytics platforms across on‑premises and AWS environments
- Designing and implementing new platform features that enhance usability, scalability and the developer experience
- Collaborating with research, data and engineering teams to accelerate time‑to‑insight through modern analytics solutions
- Driving improvements in automation, observability and resilience across analytics services
- Evaluating and adopting emerging technologies such as AI assistants, data mesh and cloud‑native analytics solutions
- Defining SLAs, KPIs and monitoring strategies to ensure reliability, security and service excellence
- Participating in the out‑of‑hours rota to support critical systems
- Experience running distributed data and analytics systems at scale using tools such as Spark, Kafka, Trino or Airflow
- Strong Linux skills and proficiency in Python for automation and integration
- Familiarity with infrastructure as code, using Terraform or Ansible
- Deep understanding of AWS analytics technologies including EMR, MSK, Athena, Redshift, Glue and MWAA
- Experience with CI/CD and observability tools such as Jenkins, ArgoCD, Prometheus, Grafana and OpenTelemetry
- Strong problem‑solving skills and a systematic approach to diagnosing and resolving issues
- Experience with streaming frameworks such as Flink, Kafka Streams and Kafka Connect
- Knowledge of modern data lake technologies including Delta Lake, Iceberg and Glue Data Catalog
- Exposure to DataOps practices and collaboration with Data Engineering teams
- Familiarity with GPU‑accelerated analytics using Spark with GPUs or RAPIDS
- Programming experience with Java, Scala, C#, Python or Go
- Highly competitive compensation plus annual discretionary bonus
- Lunch provided (via Just Eat for Business) and dedicated barista bar
- 35 days’ annual leave
- 9% company pension contributions
- Informal dress code and excellent work/life balance
- Comprehensive healthcare and life assurance
- Cycle‑to‑work scheme
- Monthly company events
G‑Research is committed to cultivating and preserving an inclusive work environment. We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation please let us know in the relevant section.
Analytics Services Platform Engineer employer: Barlowe LLP
At G-Research, we pride ourselves on being an exceptional employer, offering a dynamic work environment in the heart of London where innovation thrives. Our commitment to employee growth is evident through our comprehensive benefits package, including 35 days of annual leave, competitive compensation, and opportunities for professional development. Join us to collaborate with world-class researchers and engineers, all while enjoying a culture that values diversity, inclusivity, and work-life balance.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Services Platform Engineer
✨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 refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and contributions. This is a great way to demonstrate your expertise in distributed analytics platforms and coding skills.
✨Tip Number 3
Prepare for interviews by brushing up on technical questions related to the tools we use, like Spark and Kafka. Practice explaining your thought process and problem-solving approach, as this will impress interviewers.
✨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, it shows you’re genuinely interested in joining our team at G-Research.
We think you need these skills to ace Analytics Services Platform Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Analytics Services Platform Engineer. Highlight your experience with distributed systems, AWS, and the specific technologies mentioned in the job description. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you’re passionate about tackling complex problems in quantitative finance. Share examples of your past work that demonstrate your problem-solving skills and your ability to collaborate with teams.
Showcase Your Technical Skills:Don’t just list your technical skills; show us how you’ve applied them in real-world scenarios. Whether it’s using Spark for data processing or implementing CI/CD pipelines, we want to know how you’ve made an impact in your previous roles.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Barlowe LLP
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Spark, Kafka, and AWS services. Brush up on your Python skills too, as automation will be key. Being able to discuss how you've used these tools in past projects will show you're ready to hit the ground running.
✨Showcase Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you tackled them. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will demonstrate your systematic approach to diagnosing and resolving issues, which is crucial for this role.
✨Collaboration is Key
Since the role involves working closely with research, data, and engineering teams, be ready to talk about your experience in collaborative environments. Share examples of how you’ve successfully worked with cross-functional teams to drive projects forward and enhance usability.
✨Stay Current with Emerging Technologies
Familiarise yourself with the latest trends in analytics and cloud-native solutions. Be prepared to discuss any emerging technologies you’ve explored, such as AI assistants or DataOps practices. Showing that you’re proactive about learning will impress your interviewers.
