SUMMARY:

• Base SAS (Data Step, Macro Facility, Base Procedures), SAS/SQL (to extract information from Oracle, Teradata, DB2, and Sybase), SAS Enterprise Guide (SAS EBI client), Dynamic SAS Macro code.
• Operating Systems: UNIX and Windows.
• Teradata SQL Assistant.
• Application lifecycle management procedures and version control software.
• Agile and Jira.
• Data retrieval, data manipulation, macro programming, custom - reporting, and the computation of key business metrics (in SAS).
• Problem resolution and SAS enterprise application upgrades.
• Research, analysis, and resolution of user-specified questions (related to enterprise SAS applications).
• User acceptance testing and production validation of enterprise warehouse tables on Teradata and Hadoop platforms.
• Working knowledge in SAS Connect (for remote session processing and data transfer in a client server environment).
• Working knowledge in SAS EBI multi-tier architecture and tool suite.

PROFESSIONAL EXPERIENCE:

Confidential

Consultant

Responsibilities:

• Used SQL to construct and execute complex queries for the purpose of user acceptance testing and production validation of enterprise warehouse tables to support reporting of customer information insights and health care analytics.
• Validation included membership tables (dashboard, benchmark, and segmentation) that were designed and reformulated for optimal performance and consumption by a visual analytic platform (Tableau).
• Validation also included claim-line tables in which paid amount and utilization were adjusted for reporting as related to those customers that were fully-insured.
• Validation supported the monthly release of enterprise warehouse tables, related reports, related visual analytic content, and related software.
• Warehouse tables were remotely accessed using Teradata SQL Assistant (client tool) and ODBC.
• Validation was performed on Teradata and Hadoop platforms across environments that included development, test, and production.
• Other tools included Excel (for comparison of source and target summary data), Agile and Jira (for documentation of test cases and tracking of defects), Bit-bucket (code repository), source-to-target mapping, various data models, Putty, and Linux.

Confidential

Consultant

Responsibilities:

• Performed a significant software upgrade of an existing enterprise application that functioned to compute and report statistical measures of model performance across a series of mortgage risk models.
• The purpose of the upgrade was to augment functionality so as to enable model performance monitoring that was comprehensive of those forecasts related to default, liquidation, and prepayment.
• Code modification and unit-testing contributed to a new release of the application software (as part of a CCAR initiative).
• The work was performed using SAS, version 9.3, in a Unix-based, development environment.
• Tools and skills emphasized Base SAS, SAS Data Step, SAS Macro Language, Base SAS Procedures, SAS Output Delivery System, and SAS Connect.
• Source data consisted of loan-level detail on credit risk drivers and loan performance (actual and forecast) for a given cohort and mortgage-related portfolio in each month of the observation window.

Confidential

Consultant

Responsibilities:

• Provided quality assurance with regard to the conversion of financial information in the upgrade of a posting and billing system related to consumer credit cards.
• SAS Enterprise Guide was used on a UNIX platform to construct SAS programs and SAS SQL queries that functioned to extract, join, transform, and compare test data that originated in the pre-conversion and post-conversion schemata.
• Test data included information from DB2 tables at the account, offer, balance, and transaction levels.
• Comparative test results were documented in Excel, and defects were tracked using application lifecycle management tools.
• Specific accomplishments included the provision of source code to implement complex transformations and the identification of numerous defects in the post-conversion pricing of promotional offers.

Confidential, Columbia, MD

Consultant

Responsibilities:

• SAS (version 9.1.3) was utilized in a UNIX environment to perform significant modification and upgrade to an existing production application as related to the Physician Quality Reporting program under CMS.
• New and updated government requirements were implemented in the current program year while innovation was provided to improve both method and accuracy in achieving the required program results.
• Such results consisted of summary statistics in a series of client reports that tracked progress in quality data submissions (through a web interface) by Confidential Part B service provider groups.
• Client reports were developed in SAS and delivered in Microsoft Excel via email attachment.
• Quality data was sourced from Oracle and included beneficiary samples representing a series of disease modules and patient care measures.
• The following software tools were utilized: Base SAS, SAS Data Step, SAS Macro, SAS Procedures, SAS SQL, SAS Output Delivery System, SAS SQL Pass Through, UNIX scripts, and version control software. Application lifecycle management procedures were followed through deployment to production.

Confidential

Consultant

Responsibilities:

• Ongoing problem resolution was provided with regard to an enterprise software application that was critical in the support of a claims payment operation for high-cost patients in the stop-loss insurance industry.
• Worked directly with business customers to analyze and solve problems, leading to change requests and the implementation of upgrades as part of enterprise application releases.
• Written explanatory summaries were provided regarding the research, analysis, and resolution of problems that were user-identified.
• Problem-solving success rate was one hundred percent.
• Enterprise application programs were run on a UNIX server in a production environment and were written in Base SAS, SAS Macro, SAS SQL, and UNIX scripts (drivers).
• Numerous programs and modules comprised both batch and ad hoc (reporting) components of the system.
• Data was sourced from a corporate warehouse (Teradata) including medical claims and enrollment information and from an Oracle database including stop-loss insurance policy-related information.

Confidential, Washington, DC

Consultant

Responsibilities:

• Designed and built an analytical dataset to support time trend analysis of prices in the non-performing loan portfolio.
• The dataset was built and augmented through the development of a series of programs (in Base SAS, SAS Macro, and SAS SQL under Windows) to perform monthly processing according to specification and business rules.
• Source data was extracted and integrated from multiple systems, including a predictive pricing model and a dealer survey.
• Transformation included data aggregation across loan dimensions, computation of Confidential weighted average prices, ranking of property state by Confidential, and match-merging of results as summarized across multiple dimensions and sources of input data.
• Subsequent to transformation, the monthly, aggregate, and integral data was loaded to a (permanent) target repository in SAS.
• Subsequent to data load, additional manipulation in SAS included data transposition and export to Excel for the production of graphs that compared aggregate model and dealer prices across time and loan dimensions (such as unpaid principal balance, loan-to-value ratio, months delinquent, property state, and modification type).

Confidential, Rockville, MD

Consultant

Responsibilities:

• Designed and built an analytical dataset to support the analysis of financial and operational risk for member firms within industry segment.
• The dataset was built and augmented through the development of a series of programs (in Base SAS Version 9.1.3, SAS Macro, SAS SQL, and SAS Enterprise Guide) running on a UNIX server to perform quarterly processing according to specification and business rules. Source data was extracted and integrated from numerous relational tables comprising an enterprise Oracle database.
• Subsequent to extraction, data transformation included numerous joins on Oracle tables to correctly relate member firms, quarterly SEC filing events, and detailed financial information contained within those filings (income, assets, liabilities, net capital, and reserves).
• Computations included financial risk measures, percentiles and relative risk scores within industry segment, and overall rank based on risk within industry segment.
• The resulting statistical profiles of financial and operational risk were loaded to a (permanent) target repository in SAS.
• Subsequent to data load, results were exported from SAS to Excel for distribution to end users and were utilized to identify high-risk, high-impact member firms for onsite audits.

Confidential, Washington, DC

Consultant

Responsibilities:

• For example, SAS programming was performed to explore data in enterprise Oracle relational tables that represented accounts and transactions.
• Subsequent to data exploration in Oracle, there was an upgrade to an existing production program so as to access and accommodate the enterprise Oracle data in improving the quality and timeliness of reporting with regard to financial obligations and expenditures related to Deepwater.
• Production programs utilized Base SAS (Version 9.1), SAS Macro, SAS SQL, SAS Output Delivery System (ODS), and SAS Connect to perform data extraction, data manipulation, and the generation of HTML documents and multi-dimensional cubes that were uploaded from a database server to a SAS EBI Information Delivery Portal on a Web server. In another example, SAS programs were developed to extract, verify, transform, and load monthly data from archived text files to historical repositories in SAS that were the basis for readiness reporting across surface, aviation, people, and positions.