Mrs. Rajitha Gentyala | Data Engineering with AI | Data Engineer at Western Alliance Bank | United States
Mrs. Rajitha Gentyala is a seasoned data engineering professional with over 15 years of IT experience, recognized for her excellence in research-driven data engineering integrated with Artificial Intelligence. Mrs. Rajitha Gentyala earned her Bachelor of Technology in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University (JNTU) Hyderabad, India, and was awarded the Certificate of Completion of Training (CCT) in July 2023. Professionally, Mrs. Rajitha Gentyala currently serves as Staff Engineer I at Western Alliance Bank and has previously contributed her expertise at Coolsoft LLC, Saipsit Inc, and Cognizant Technology Solutions, demonstrating strong leadership and technical proficiency. Mrs. Rajitha Gentyala’s research interests focus on AI-enabled data pipelines, ETL optimization, cloud analytics, and data quality engineering. Her research skills include Azure Synapse, SQL, ETL tools, Power BI, Unix scripting, and agile methodologies. Mrs. Rajitha Gentyala has received the Excellence in Research Award, reflecting her impactful contributions and continued dedication to advancing data engineering and applied AI innovation.
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Featured Publications
Ethical Artifacts: Engineering Verifiable Audit Trails for Human-in-the-Loop Decisions in ML Data Pipelines
– Journal of Scientific and Engineering Research, 2025
Bridging the Semantic Divide: A Framework for Cross-Functional Literacy Between Data and Machine Learning Engineers
– European Journal of Advances in Engineering and Technology, 2025
Mapping Imperfections to Instruments: A Unified Taxonomy for Data Engineering in Behavioral Economics
– International Journal of Data Engineering Research and Development (IJDERD), 2025
Benchmarking Prompt Architectures: A Quantitative Study of Contextual and Decomposed Prompting for Complex ETL Code Generation
– International Journal of Computer Science and Engineering (ISCSITR-IJCSE), 2025
From Pipelines to Predictions: An Empirical Study on the Critical Behavioral Markers and Skill Pathways for Effective AI Data Engineering
– Journal of Scientific and Engineering Research, 2024