The ideal candidate is a reliable, fast learning, efficient system engineer / developer with hands-on experience in building custom infrastructure solutions, focuses on automation, and is proficient in Python, Linux, and core infrastructure concepts.
The candidate will be responsible for providing infrastructure engineering and support for Morgan Stanley’s data analytics and test data management platforms as well as developing and maintaining self-service and other tooling to support their seamless integration.
Other responsibilities may include escalation handling, troubleshooting, monitoring, optimizing and tuning, establishing best practices, and supporting new user on-boardings.
Essential Skills:
Proficiency in Python and Unix shell scripting- Understanding of core infrastructure fundamentals (OS, networking, storage, virtualization, AuthN/AuthZ, APIs, etc.).
Comfortable in following Agile/DevOps practices with relevant experience in using tools such as Git, Jira, and Bitbucket.
Software developer experience and understanding of fundamentals in distributed system design, development, and deployment.
Hands-on experience with at least one major DBMS, data analytical, and/or messaging products (Sybase ASE, DB2 UDB, MSSQL, PostgreSQL, MongoDB, Greenplum, Redis, Hadoop, Databricks, MQSeries, Kafka).
A self-starter with the ability to work effectively in teams.
Excellent verbal and written communication skills.
Desired Skills:
Experience with container platforms like Docker and Kubernetes.
Experience with Public Cloud services, terminology and security/control requirements.
Experience in designing and managing applications in large-scale distributed environments.
Experience with system performance and tuning.
Hands-on experience with Test Data Management, Data Masking, ETL, and/or Data Analytics platforms (Delphix, Informatica, Dataiku, Alteryx, Talend, etc.)