Joseph Aaron Tsapa
Biography
Joseph Aaron Tsapa is a strategic technology leader who shapes enterprise data and AI agendas and turns complex regulatory requirements into scalable, outcome-driven systems. He has led transformations across banking, financial services, healthcare, and utilities -aligning analytics, governance, and engineering to deliver measurable business value. At the helm of major programs, Joseph has modernized risk and regulatory reporting, built robust data-governance foundations, and executed digital-consolidation initiatives that shorten validation cycles and lift productivity. He has championed automation across cloud, ETL, reporting, and database platforms, and pioneered the use of large language models (LLMs) in data engineering and operations-setting new benchmarks for data quality, efficiency, and cost. His leadership in AML/BSA operations and regulatory reporting has helped redefine compliance practices in banking. He serves on editorial boards and as a reviewer for international journals and has earned multiple industry awards. Joseph holds a Doctor of Advanced Studies (D.A.S.; honorary Dr. h.c.) in Computer Science from Azteca University, a Professional Doctorate (PD) in Computer Science from European International University (EIU) - Paris, and a Master of Science (M.S.) in Software Engineering from the Birla Institute of Technology and Science (BITS), Pilani. A prolific author and recognized thought leader, Joseph has published more than 25 papers and is the author of the books Generative AI: Concepts and Applications, Practical Machine Learning: Real-World Applications and Techniques, and Data Science & Machine Learning: The Modern Practitioner?s Guide.
Research Interest
Artificial Intelligence, Machine Learning, Data Science, Data Engineering
Abstract
Artificial Intelligence in Data Engineering: Use Cases, Challenges, and Future Directions
AI is reshaping data engineering from batch plumbing to adaptive, feedback-driven systems. The talk maps concrete points of impact across the pipeline: LLM-assisted ingestion and schema inference; automatic ELT code synthesis and transformation validation; contract-aware quality checks with anomaly and drift detection; lineage reconstruction and metadata enrichment; cost- and carbon-aware orchestration; and policy-as-code access controls. Case patterns highlight measurable outcomes-faster onboarding of new data sources, lower incident rates, and improved reliability of downstream analytics and ML.
Adoption remains nontrivial. Key risks include hallucinated mappings, brittle prompts, hidden data leakage, unclear accountability in human-AI handoffs, and regulatory exposure under privacy and sovereignty rules. An evaluation-first approach is outlined: task-level metrics (e.g., precision/recall for anomaly detection, F1 for mapping), shadow and canary deployment, rollback strategies, and red-teaming of prompts and models. A reference architecture separates a governed control plane (model/prompt registry, guardrails, policy engine, audit) from an execution plane (DAG compiler, vectorized retrieval for context, observability with lineage-aware alerts), with human-in-the-loop checkpoints anchored in data contracts and SLAs.
Looking ahead, the field is trending toward agentic pipelines that negotiate data contracts, privacy-preserving transformation (differential privacy, federated execution, synthetic data), multimodal ETL, standardized benchmarks for DE tasks, and greener scheduling objectives. Attendees gain a decision framework-what to automate, how to measure it, and how to scale it safely-without sacrificing governance, cost control, or trust.
