Patricia Hensley

Alexandria, Virginia

Email: adoctq@r.postjobfree.com

TECHNICAL AND FUNCTIONAL EXPERTISE

Base SAS (Data Step, Macro Language, Base Procedures), SAS/SQL (to extract information from Oracle, Teradata, DB2, and Sybase), SAS Enterprise Guide (SAS EBI client)

Data retrieval, data manipulation, macro programming, custom-reporting, and the computation of key business metrics

SAS enterprise application upgrade and enhancement

Research, analysis, and problem resolution (related to enterprise SAS applications)

Complex SQL queries for user acceptance testing and production validation of enterprise warehouse and data-mart tables on Teradata, Hadoop Hive, and DB2 platforms.

Working knowledge in Agile methodology, Jira, Gitlab, Confluence, Bitbucket

Working knowledge in HP Application Lifecycle Management

Teradata SQL Assistant

Working knowledge in operating Systems: Unix, Linux, and Windows

PROFESSIONAL EXPERIENCE

U.S. Census Bureau, Suitland, MD, SAS Developer, 2019-2021

In a developer role, provided significant software upgrade of an existing enterprise application that delivered multiple codebooks via browser-based requests, web technologies, and backend calls to SAS. Provided enhancement, analysis, and improvement of the existing application to meet requirements that originated across customer teams in the directorate. Used SAS 9.4 technologies that enabled source data extraction and manipulation, codebook reporting and formatting, and output delivery in JSON, PDF, and CSV. Devised, implemented, and unit-tested code modifications to produce software that was parameterized, dynamic, and maintainable. Experience entailed an Agile environment using GITLAB as a tool to manage the development, version control, and release of software. Expertise and skills included: Base SAS, SAS Data Step, SAS Macro Language, SAS Procedures, SAS SQL, SAS INTRNET, SAS Output Delivery System (ODS), SAS ODS Report Writing Interface (RWI), SAS JSON Procedure, and Linux shell scripts.

The Anthem Companies, Inc., Columbia, MD, Business Information Developer Consultant, 2017-2019

Used SQL to develop and execute complex queries for the purpose of user acceptance testing and production validation of enterprise warehouse and data-mart tables. Testing and validation included membership tables (dashboard, benchmark, and segmentation) that were designed and reformulated for optimal performance and consumption by a visual analytic platform. Testing and validation also included claim-line tables in which paid amount and utilization were adjusted for reporting related to those customers that were fully insured. Testing and validation supported the monthly release of enterprise warehouse tables related to customer information reporting and health care analytics. Warehouse tables were accessed remotely and tested on Teradata, Hadoop Hive, and DB2 platforms across environments that included development, test, and production. Other tools included Microsoft Excel, Microsoft Access (for comparison of source and target summary data), Jira (for documentation of test cases and tracking of defects), Confluence, Bitbucket (code repository), Teradata SQL Assistant, Agile methodology, functional requirements, source-to-target mapping, data model, and metadata repository.

JP Morgan Chase & Company, Columbus, OH, SAS Developer, 2014-2016

Performed a significant software upgrade of an existing enterprise SAS 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 and enable model performance monitoring that was comprehensive of those forecasts related to default, liquidation, and prepayment. Developed, modified, and unit-tested code (across numerous SAS programs and modules) that 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. 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. Tools and skills emphasized Base SAS, SAS Data Step, SAS Macro Language, Base SAS Procedures, SAS Output Delivery System, and SAS Connect.

Bank of America, Wilmington, DE, SAS Developer/Analyst, 2013-2014

Provided quality assurance and testing in the conversion of financial data related to the upgrade of a posting and billing system in the domain of consumer credit cards. SAS Enterprise Guide was used on a UNIX platform to develop SAS programs and 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 SAS source code to implement complex transformations and the identification of numerous defects in the post-conversion pricing of promotional offers.

Centers for Medicare & Medicaid Services, Columbia MD, SAS Developer, 2012-2013

SAS (version 9.1.3) was utilized in a UNIX environment to perform development, modification, upgrade, and unit-testing of an existing production SAS application related to the Physician Quality Reporting program under CMS. New and updated government requirements were implemented in the current program year while code 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 Medicare 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 Language, 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.

Highmark Blue Cross Blue Shield, Camp Hill, PA, SAS Analyst, 2011-2012

Ongoing problem resolution was provided regarding 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 Language, 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.

Federal National Mortgage Association, Washington D.C., SAS Developer, 2010-2011

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 Data Step, SAS Procedures, 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 UPB weighted average prices, ranking of property state by UPB, and match-merging of results as summarized across multiple dimensions and sources of input data. After transformation, the monthly, aggregate, and integral data was loaded to a (permanent) target repository in SAS. After data load, additional manipulation in SAS included data transposition and export to Excel to produce 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).

Financial Industry Regulatory Authority, Rockville, MD, SAS Consultant, 2008-2009

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. After 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. After 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.

United States Coast Guard, Washington DC, SAS Consultant, 2006-2007

SAS services were carried out (in a SAS EBI environment) regarding existing production programs that were run on a scheduled basis to track and report resources related to the USCG Deepwater acquisition and modernization program. For example, SAS programming was performed to explore data in enterprise Oracle relational tables that represented accounts and transactions. After data exploration in Oracle, there was an upgrade to an existing production program to access and accommodate the enterprise Oracle data in improving the quality and timeliness of reporting 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, integrate, 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.

GMAC RFC Securities, Bethesda, MD, SAS Analyst, 2005-2006

Used Base SAS (Version 9.1) on a Windows XP platform to carry out tasks that included both ad hoc programming and the new development of production reports to support the business areas of risk management, finance, and trading. Specific accomplishments included the development of programs to generate flagship risk reports that were run daily to measure compliance in the areas of securities portfolio size, age, and exposure to market risk factors. End-user reports were developed in SAS, exported to MS Excel, and formatted using Dynamic Data Exchange (DDE). Also worked with existing source code to acquire knowledge of current processes to modify, maintain, and improve those processes. For example, SAS programs extracted current-day portfolio snapshots from multiple flat files and subsequently transformed, manipulated, and loaded such data to permanent historical repositories in SAS. Historical repositories were the basis for daily, weekly, and monthly enterprise reporting.

EDUCATION B.S. Economics, University of Delaware, Newark, Delaware

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