The global debt market has more than tripled in size the past 15 years and currently exceeds over $100 trillion. During the same span, computerized trading platforms have accounted for an ever-increasing volume of transactions. Now more than ever, it is critical we pursue every measure to increase the time-to-market and accuracy of the underlying data...the data that both humans and computers use to make investing decisions that direct the flow of hundreds of billions of dollars each day. Come join the team responsible for this data and help it to continue to drive its ETL solutions to the leading edge.

There can be many challenging - and interesting - ways to increase processing speed and data accuracy:

• Automating now-manual data processing using various machine learning, heuristic, and rules-based techniques
• User interface improvements to the current suite of apps used for manual data processing
• Refactoring and redesign of existing back end services

And the beauty of it all? We do not intend to dictate a solution to you. Rather, we will provide you with the opportunity to explore various avenues for improving our current processing.

As an engineer in Data Technologies, you’ll be responsible for the systems that onboard all the referential data that drive Bloomberg's applications and enterprise systems. As our clients are shifting more and more to rely on machines to interpret data and drive insights, we are utilizing cutting edge technologies to deliver unparalleled data quality. By joining Data Technologies, you will help us improve the accuracy, coverage, timeliness, and accessibility of our data to service our clients across all of Bloomberg's products. Learn more about the Data Technologies teams at Princeton here: https://www.youtube.com/watch?v=qtUu9LCNmiU

You'll need to have:

• Proficiency with at least three of the following: C, C++, JavaScript, Python
• Experience developing on both Unix and Linux platforms
• The ability to independently conceive of and implement efficient, pragmatic solutions to complex problems
• Experience developing quality software through code reviews, unit testing, and continuous integration
• Experience with at least two of the three phases of ETL
• BA, BS, MS, PhD in Computer Science, Engineering or related technology field

If this sounds like you, we want to talk!