System Engineer

Jovita Mendonca

Dallas, TX 551-***-**** *******.**@*****.*** linkedin.com/in/jovita-mendonca-994922288/ Skills

Languages: COBOL, JCL, SQL, NoSQL, Easytrieve, Rexx, Python, R, HTML, CSS, Javascript Databases: DB2, IMS, VSAM, Oracle, MongoDB, Microsoft SQL Server Operating Systems: z/OS, MVS, Windows

Tools: TSO/ISPF, CICS, SPUFI, FILEAID, SPOOL, Fault Analyzer, XPEDITOR, Beta 92, OPC, ISPW, ISPF ETL Tools: Informatica Power Center, Azure Databricks Certifications: Oracle DBA Foundation, Application Programming Interface, Six Sigma Lean Professional, Microsoft Azure Fundamentals, IBM Data Science, IBM Data Engineering, Teradata SQL for Data Science and Analytics Other Technologies: JIRA, ServiceNow, Confluence, Postman, Topaz Workbench, Azure DevOps, Automation Anywhere, Microsoft Office, Power BI, Shell Scripting, Tableau, SAS, UIPath

Work Experience

Credit Union of Texas, Allen, TX Jul’24 – Sept’24

Analytics Intern

Managed MSSQL databases and deployed machine learning models using Python and Azure ML.

Developed and designed Role Based Access Control mechanisms in Azure to effectively onboard users and apply the principle of least privilege as per cybersecurity standards thereby enabling process simplification by 20%.

Automated accounting processes report generation to represent real-time financial KPIs report using Power BI and SSRS. Tata Consultancy Services, India Mar’20 – Jul’23

System Engineer (Client: A leading global financial institution with over EUR 150 billion in assets under management)

Developed secure COBOL batch applications and conducted end-to-end testing to ensure compliance with GDPR privacy legislation, effectively cleaning up customer personal data and enhancing data security.

Automated process workflows using Bash scripts in Informatica, reducing manual monitoring by 70%.

Conducted root cause analysis using Xpeditor and DB2 (SPUFI) to identify and rectify incorrect FATCA profiles assigned to customers, ensuring regulatory compliance and improving data accuracy by 12%.

Designed and developed data pipelines to ingest financial data from multiple sources into Azure Databricks Lakehouse for various data science use cases, thereby reducing manual efforts by 20% and saving resources by 30%.

Optimized legacy transaction processes by identifying obsolete components and refining SQL queries, resulting in reduction in CPU usage and monthly cost savings of EUR 5,000 (~$5350).

Developed z/OS APIs for IMS transactions using z/OS Connect and automating load testing with Postman.

Developed ETL mappings and monitored workflows in Informatica PowerCenter to transform and load data from multiple sources, ensuring data integrity and timely execution.

Provided L3 production support using ServiceNow, Spool, Fault Analyzer, and Easytrieve for incident management, job monitoring, defect resolution, and data reporting, enhancing system performance by 35%.

Designed and implemented an online application to identify and delete inactive client contracts from IMS databases, utilizing DL/I calls for retrieval and removal of unused records.

Tata Consultancy Services, India Jul’19 – Feb’20

System Engineer Intern

Assisted in creating unit test plans, executing system testing, and managed batch cycle execution using COBOL, JCL and OPC.

Designed, developed, and maintained transaction processing applications using Telon screens, improving user interface.

Streamlined the delivery of customer financial files by optimizing processes, resulting in a 2.5-hour reduction in delivery time leading to a 25% increase in on-time deliveries.

Provided support in developing and monitoring MQ applications for seamless file transfers.

Recompiled 100+ COBOL online and batch programs from v4.2 to v6.3, enhancing performance and compatibility with updated systems. Education

University of Texas at Dallas – Jindal School of Management, Dallas, USA May 2025 M.Sc. Financial Technology and Analytics GPA 3.54

University of Mumbai – St. Andrew’s College, Mumbai, IN Apr 2019 Bachelor of Commerce, Major in Financial Accounting and Auditing GPA 3.08 Academic Project

Default Risk Model for Credit Approval Sep’23 – Oct ‘23

Utilized advanced machine learning techniques, like XGBoost and Neural Networks, to analyze extensive customer data from 450K American Express clients and conducted SHAP analysis to interpret model predictions and enhance transparency, achieving a 94% AUC in predicting credit defaults, leading to a revenue maximization of $373M for American Express lending practices.

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