Overview
On Site
Full Time
Skills
Real-time
Market Analysis
Partnership
Acquisition
Data Processing
Banking
Bloomberg Terminal
Fixed Income
Mutual Funds
Pricing
Research
Management
Art
Large Language Models (LLMs)
Natural Language Processing
Algorithms
Bloomberg
Apache HBase
Apache Hive
MySQL
Apache Spark
Kubernetes
Business Intelligence
Research Design
Finance
Open Source
Business Data
Agile
Collaboration
Software Development
Client/server
Python
C
C++
JavaScript
TypeScript
GraphQL
RESTful
UI
User Experience
Systems Design
Big Data
SQL
NoSQL
Database
Cloud Computing
Computer Science
Mathematics
Microservices
Caching
Data Science
Machine Learning (ML)
Job Details
Bloomberg delivers billions of data points to our customers each day and our clients heavily rely on comprehensive financial information including real-time market data to identify trends and gain data transparency so they can analyze the value of a potential investment, partnership, or acquisition target. In order to make this analysis possible for our clients, the Data Technologies Engineering department is responsible for designing and building large-scale sophisticated infrastructure and data processing systems to acquire financial reference information and pricing data from sources around the world (regulatory agencies, exchanges, central banks, buy-side and sell-side firms, websites, news, feeds, etc).
The data processed through our systems is the lifeblood of the Bloomberg Terminal as well as Enterprise solutions across all the major asset classes including Equity, Fixed Income, Mutual Funds/ETFs, Mortgages, Futures, Options, Indices, and Portfolios/Holdings. In addition to these major asset classes, we provide news, pricing, company and industry research, biographical and company management information, platforms and tools to help onboard securitized and non-securitised data for the major players in the financial markets.
Due to the nature and sensitivity of our data to the market, performance, reliability and quality are key features of our systems. We apply state-of-the-art techniques (including Large Language Models, Natural Language Processing (NLP), and Heuristic algorithms) to extract, standardize, enrich, and publish billions of data points that fuel Bloomberg products. Our services leverage big data technologies such as HBase, Hive, MySQL Vitess, and Spark as well as a scalable Kubernetes-based microservice platform and a bi-temporal Point In Time (PIT) database solution.
We are a technologically diverse group solving a number of challenging problems using innovative techniques. We are proud of our intellectual curiosity and the quality of our product. We share a passion for solving real life problems and provide pragmatic solutions by rolling up our sleeves and collaborating seamlessly, within and across teams, to provide our clients with accurate, timely data to make critical investing decisions.
What you will do:
As a member of our department, you will have the opportunity to research, design and develop high quality, low latency, distributed, and scalable self-service platforms and mission-critical applications that provide financial data delivery to our customers around the globe. This includes developing innovative solutions to provide automation as well as manual processing utilizing a variety of proprietary and open source technologies on microservices-based architecture with generic and extensible data models stored in big data platforms. You will work closely with Business, Data, and other Engineering teams globally in a hybrid environment on all phases of software development using Agile methodologies. You will take full ownership of products and collaborate with team members and stakeholders to shape the team's direction, culture, and vision.
You'll need to have:
Nice to have (you will have opportunity to learn):
Experience working with Microservice architectures and methodologies such as Twelve-factor and Lambda
Familiarity with Transport and Application layer protocols
Understanding of queuing and caching technologies
Experience with Data Science and Machine Learning techniques to help with automation.
The data processed through our systems is the lifeblood of the Bloomberg Terminal as well as Enterprise solutions across all the major asset classes including Equity, Fixed Income, Mutual Funds/ETFs, Mortgages, Futures, Options, Indices, and Portfolios/Holdings. In addition to these major asset classes, we provide news, pricing, company and industry research, biographical and company management information, platforms and tools to help onboard securitized and non-securitised data for the major players in the financial markets.
Due to the nature and sensitivity of our data to the market, performance, reliability and quality are key features of our systems. We apply state-of-the-art techniques (including Large Language Models, Natural Language Processing (NLP), and Heuristic algorithms) to extract, standardize, enrich, and publish billions of data points that fuel Bloomberg products. Our services leverage big data technologies such as HBase, Hive, MySQL Vitess, and Spark as well as a scalable Kubernetes-based microservice platform and a bi-temporal Point In Time (PIT) database solution.
We are a technologically diverse group solving a number of challenging problems using innovative techniques. We are proud of our intellectual curiosity and the quality of our product. We share a passion for solving real life problems and provide pragmatic solutions by rolling up our sleeves and collaborating seamlessly, within and across teams, to provide our clients with accurate, timely data to make critical investing decisions.
What you will do:
As a member of our department, you will have the opportunity to research, design and develop high quality, low latency, distributed, and scalable self-service platforms and mission-critical applications that provide financial data delivery to our customers around the globe. This includes developing innovative solutions to provide automation as well as manual processing utilizing a variety of proprietary and open source technologies on microservices-based architecture with generic and extensible data models stored in big data platforms. You will work closely with Business, Data, and other Engineering teams globally in a hybrid environment on all phases of software development using Agile methodologies. You will take full ownership of products and collaborate with team members and stakeholders to shape the team's direction, culture, and vision.
You'll need to have:
- 4+ years hands-on software development experience in building well designed and robust client/server-side applications with Python, C/C++, Javascript/Typescript
- Experience building gPRC/GraphQL/REST services
- Functional expertise building microservices
- Familiarity with User interface (UI) design and user experience (UX) principles
- Prior contributions to system design and architecture and scaling fault-tolerant, distributed systems
- Knowledge of Big Data, SQL and NoSQL Databases, and Cloud-Based Object Stores
- A Degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience
Nice to have (you will have opportunity to learn):
Experience working with Microservice architectures and methodologies such as Twelve-factor and Lambda
Familiarity with Transport and Application layer protocols
Understanding of queuing and caching technologies
Experience with Data Science and Machine Learning techniques to help with automation.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.