Enterprise Architect - Data & Analytics - Life Sciences
Inseriert am: 24.09.2018
Ranked as #12 on Forbes’ List of 25 Fastest Growing Public Tech Companies for 2017, EPAM is committed to providing our global team of over 25,900+ EPAMers with inspiring careers from day one. EPAMers lead with passion and honesty, and think creatively. Our people are the source of our success and we value collaboration, try to always understand our customers’ business, and strive for the highest standards of excellence. No matter where you are located, you’ll join a dedicated, diverse community that will help you discover your fullest potential.
DESCRIPTION
Currently we are looking for an Enterprise Architect - Data & Analytics - Life Sciences for our Basel office to make the team even stronger.
We are willing to build and strengthen our Big Data Enterprise Architects teams for Life Sciences. Our ideal candidate will possess skills that make them a subject matter expert in various big data technologies and data science. These roles will entail architecture analysis, design, client management, and other skills.
Requirements
Practical experiences working in Life Sciences/Healthcare industry
Strong knowledge of programming and scripting languages such as Java, Python, Ruby, or R
Experience with major big data technologies and frameworks including but not limited to Hadoop, MapReduce, Pig, Hive, HBase, Oozie, Mahout, Flume, ZooKeeper, MongoDB, and Cassandra
Experience with big data solutions developed in large cloud computing infrastructures especially Amazon Web Services
Experience in client-driven large-scale implementation projects
Data Science and Analytics experience is a plus (machine learning, recommendation engines, search personalization)
Technical team leading and team management experience, deep understanding of Agile (Scrum), RUP programming process
Strong experience in applications design, development and maintenance
Solid knowledge of design patterns and refactoring concepts
Practical expertise in performance tuning and optimization, bottleneck problems analysis