Senior Capacity Planning Engineer

The Senior Capacity Planning Engineer for Mainframe works with a team responsible for monitoring, analyzing, and reporting mainframe processing capacity. The successful candidate will collect, process, and validate system resource data and business volume data for future analysis using sound data science principles. This role requires a basic understanding of Mainframe and knowledge of the sources and tools used to collect, validate, and process system data.

Key Responsibilities
Run and validate the Data Collection and Reporting process using scheduling software.
Design basic reports using SAS and build and edit JCL in ISPF.
Create and update policies, job aids, and other documentation.
Learn MICS/MXG Database Architecture and diagnose MICS-related errors.
Execute and validate MICS administrative tasks to maintain proper maintenance.
Use basic methods to summarize data, display it in an organized manner, and identify patterns and relationships.
Collect and report business volume data from various sources.
Conduct risk assessments of extreme market conditions for selected business streams.
Produce and update a Business Process Requirements document for selected business streams.
Organize and store Capacity Planning artifacts for future retrieval.
Develop ad hoc reports for senior business and management forums.
Mitigate risk by following established procedures, monitoring controls, and spotting key errors.
Required Qualifications

Education: A bachelor's degree in a mathematics or statistics discipline is preferred, or equivalent work experience.

Experience: A minimum of 5 years of related experience is required.

Technical Skills:
SAS experience is required.
An understanding of mainframe technology and its components is necessary.
MICS/MXG experience is essential.
Familiarity with mainframe capacity and performance data (SMF, RMF, CICS, DB2, MQ) is needed.
Intermediate skills in MS Office, particularly PowerPoint and Excel, are required.
Analytical skills including data exploration, analysis, and production of models are required.
A solid background with Descriptive Statistics is required.
Preferred Qualifications
Knowledge of Inferential Statistics is a plus.