Posted Aug 25, 2017 - Requisition No. 60913
The Bloomberg Query Language (BQL) team is pushing the envelope to lead the low-latency analytics space in the financial domain. We are developing a cloud-based low-latency Analytics and Screening Platform for huge financial data sets. We are also creating a Financial Query Language to allow users to express complex data retrieval, analytics and screening for processing on the BQL Platform. Aside from analytics, the client query language can be used to express complex screening capabilities.
Our team is architecting and designing the entire ecosystem for the Analytics Platform and Screener Engine. The platform uses Hadoop-based distributed analytics and computing technologies such as Spark, Hive and HBase. We also need to build a test framework for this platform. That’s where you come in.
As a QA Automation engineer, you will help build a Python-based framework for automated testing and data quality assurance. If you have an aptitude for problem solving and are eager to measure the quality of big data and analytic systems, we’d love to talk to you!
You’ll learn NumPy, SciPy, Pandas and other Python modules if you aren’t already proficient in them. And, since our group works with diverse technologies including Java, Python, Scientific Computing Libraries, Jenkins, Spark and Solr, there are plenty of avenues to innovate and contribute to the open source community.
If this sounds exciting, submit an application. You can also watch Partha, our team lead, discuss how we’re using Spark for Dynamic Composable Analytics at Bloomberg (https://www.youtube.com/watch?v=LOIvs_JhrY0&feature=youtu.be).