Candidates hired for this role will be required to start onsite from Day 1 following hybrid work option, 2 days in office. Please submit candidates who are local to either Dallas, Tampa or Jersey.

Due to a recent increase in misrepresentation during the interview process, Company has implemented a new policy that requires a candidate’s headshot on the front page of the resume to be considered.

This role is contract to hire!

The Principal Capacity Planning Engineer for Mainframe works directly with a team responsible for monitoring, analyzing, and reporting mainframe processing capacity. The successful candidate will collect system resource data along with business volume data for analysis using sound data science principles. One needs to be knowledgeable of which types of data are used in Capacity Planning and well as the sources and tools used to collect, validate, then process required data. One should be familiar with Mainframe infrastructure components. A strong mathematics background in both descriptive and inferential statistics is imperative to extrapolate insight from large quantities of raw data using SAS software on the mainframe and in distributed environments. A solid foundation with Excel is a plus.

Monthly Mainframe Capacity Reports including

• Measurement of system resources usage for all LPARs
• Usage Trends, Top-N lists, Peak Profiles, Threshold Exception, etc.
• Data analysis – Descriptive and Inferential Statistics
• Correlation Analysis, Business volume to System Resources
• Pattern Recognition / Detection of outliers
• Linear / Logistic Regression, ARIMA forecasts, integrating projections with forecast data

MICS support and maintenance

• Ensure the successful daily processing / reporting of 30 different MICS units split by LPAR and work type (Examples: SMF/RMF component, CICS, DB2, MQ, mainframe web logs)
• Apply MICS maintenance to support new processors, z/OS operating systems and general updates
• Ad-hoc reporting inquiries such as frequency of a specific function, transaction, or job

MUST HAVE

Experience collecting system resource data along with business volume data for analysis using sound data science principles.

knowledgeable of which types of data are used in Capacity Planning and well as the sources and tools used to collect, validate, then process required data.

Familiarity with Mainframe infrastructure components.

A strong mathematics background in both descriptive and inferential statistics is imperative to extrapolate insight from large quantities of raw data using SAS software on the mainframe and in distributed environments.

A solid foundation with Excel is a plus.