Bloomberg: Software Engineer - SRE - Ticker Plant
731 Lexington Ave
New York, NY 10022


Employee Testimonials

Bloomberg Testimonial
Bloomberg Testimonial
Bloomberg Testimonial

Our Team:

Bloomberg is the premier provider of real-time market data to the financial world. The Ticker Plant group is at the core of both the Bloomberg Professional Service and Enterprise Solutions products that process market data from around the globe. Our systems process over 80 billion events a day publishing these in real-time while servicing millions of client queries from our cutting edge time series database. As SREs, we are tasked with applying software engineering principles to solve the problems of owning large, expanding market data systems while ensuring we maintain resiliency, efficiency, availability and visibility at any scale. Within our group, the SRE Team is comprised of experts in various specialties like software engineering, platform performance and automation ... and we're growing!

What's in it for you:

On our team, you'll design and develop scalable services that enhance the stability and reliability of Bloomberg's market data infrastructure. We'll depend on you to not only help set standards but also partner closely with our application engineers to ensure that all products meet those standards. You'll be trusted to create engineering solutions to operations problems, build systems capable of early detection of issues through metrics and signals and develop automated correction and remediation strategies.

We'll trust you to:

  • Create solutions to monitor the health, availability, latency and reliability of our services with a focus on fault tolerant approaches
  • Proactively scale our services to stay ahead of ever increasing market data demands by driving capacity planning, instrumentation and performance analysis
  • Ensure service issues do not reoccur by architecting automation and remediation strategies employing signal detection and orchestration frameworks
  • Define service level objectives and drive measurable service improvement

You'll need to have:

  • 3+ years professional work experience
  • Proficiency in one or more of these high level languages: Python, C++, or Java - Experience with working in and maintaining a large code base
  • Good understanding of data structures and algorithms
  • Strong understanding of large-scale systems architecture
  • Working knowledge of UNIX/Linux
  • Strong Communications skills
  • Excellent problem solving skills, experience with triaging and solving production outages and a strong sense of ownership
  • BA, BS, MS, PhD in Computer Science, Engineering or related technology field

We'd love to see:

  • Versatile in one or more scripting languages (Python, Perl)
  • Building orchestration systems (Ansible, Salt, etc)
  • Config management (Chef, puppet, CFEngine)
  • Knowledge of test frameworks (GTest, etc)
  • Familiarity with industry standard tools for collection of data across distributed systems, such as Splunk, Grafana, ElasticSearch, Nagios, Zabbix, etc
  • Experience with incident response and blameless postmortems

If this sounds like you:

Apply if you think we're a good match! We'll get in touch with you to let you know what the next steps are.

In the meantime, check us out at:


Employee Testimonials

Bloomberg Testimonial

I’ve been at Bloomberg for almost 5 years now and throughout my time in Analytics, I learned an immense amount about the Customer Service industry. Particularly, how clients interact with client service representatives and through technological means. It also connected me to many different departments throughout the organization, and that knowledge and network has helped me drive results involving multiple stakeholders.

Bloomberg Testimonial

I get uncomfortable if I start feeling too comfortable at work. Luckily there is no shortage of exciting challenges here.

Bloomberg Testimonial

I really enjoy working on stories that allow me to collaborate with other Bloomberg reporters to reveal deep insights on the largest operators in the credit space.