Demonstrated data engineering experience, utilizing agile methods and modern SDLC processes to deliver quality technological solutions in a transparent, reliable way.
Experience in building and designing solutions for data warehouse and experience in working with large data sets in Oracle and Snowflake
Demonstrable mastery of industry best practices in the data lifecycle, including data quality automation and tooling.
A proven track record of working with complex data environments and associated technology and analytics infrastructure needed to support these environments. Ability to recognize business risk and surface it to key decision-makers.
Domain experience with financial datasets like FactSet, Bloomberg, IBES, CompStat, S&P Global, Worldscope, Morningstar, MSCI, Reuters, IDC, Markit, BARRA, Axioma, Northfield, and PORT is a plus
Demonstrated Experience in Python programming and familiarity with core data science libraries. Understanding different package managers and test frameworks.
Experience in some of the following technologies continuous integration/delivery tools such as GIT, Cloudbees Jenkins, uDeploy and/or Pivotal Concourse.
Knowledge in python web frameworks like Flask, Django is a plus
Fluency in formal language design concepts like type systems a plus.
Experience with quant research processes, methodologies, and tools a plus
Senior Quant Data Engineering