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Applied Data Engineer
Applied Data Engineer at Morgan Stanley building financial RAG pipelines that improved retrieval relevance by 20–25%.
United StatesApplied Data Engineer at Morgan Stanley
Designed and operated production data solutions using Python, SQL, Azure Blob Storage, and Azure AI Search, integrating structured and unstructured financial data. Built scalable batch ingestion and ETL pipelines supporting over 1M document chunks, and developed RAG pipelines using Azure OpenAI and LangChain.
Python
Engineered and maintained ETL pipelines using Ab Initio, Python, and SQL, processing millions of financial records weekly into an Oracle Data Warehouse. Developed parameterized Azure Data Factory pipelines and dbt models, and optimized batch processing to reduce time by 20-25%.
Ab InitioPythonSQLOracleAzure Data FactoryData Lake Storage Gen2SnowflakeDBTAzure Key Vaultazure monitorAutosysPandasData ProfilingData QualityAgile Scrum
Developed and optimized SQL data pipelines delivering validated banking data to 10+ business-critical BI dashboards. Refactored long-running queries to reduce execution time by 20-30% and implemented automated data-quality checks.
SQLAzure Data FactoryAzure Data Lake StoragePower BIData QualityRoot Cause AnalysisStored ProceduresQuery Optimization
Analyzed financial and operational records using SQL, Python, R, and Excel. Built scheduled ETL workflows and automated reports, reducing manual effort by approximately 30% and improving reporting accuracy.
SQLPythonRExcelPandasNumPyPower BITableauMySQLPostgreSQLETLData Analysis
Pursued a Master's degree in Computer Science at the University of Central Missouri.