AI-driven data engineering: Automating the future of ETL and data quality at scale

5^th International Congress on AI and Machine Learning

December 09-10, 2024 | Dubai, UAE

Raghavender Maddali

Software QA Engineer, Staff Move Inc, USA

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

Statement of the Problem: As organizations scale, the demand for highly efficient, accurate, and intelligent data pipelines becomes critical. Traditional ETL processes and manual data quality checks fall short in addressing the velocity, volume, and variety of modern enterprise data. This paper presents an AI driven approach to automating ETL pipelines and enforcing data quality at scale using machine learning, reinforcement learning, and neural-symbolic AI models. By integrating intelligent transformation logic, automated anomaly detection, and self healing pipelines, the proposed framework reduces human intervention, improves accuracy, and enables continuous data validation. We demonstrate how AI-enhanced data engineering improves processing efficiency, dynamically adapts to schema changes, and empowers downstream analytics in real-time environments. Real-world implementations on cloud-native architectures, including Snowflake and AWS Glue, showcase significant gains in performance, scalability, and trust in enterprise-grade data workflows. This work contributes to the evolution of autonomous data engineering systems, paving the way for resilient, AI-first data infrastructures. I would like to begin with an official disclaimer: Due to the sensitive nature of this project and the stakeholders involved, this initiative remains highly classified and confidential until the completion of all phases. While I am providing as much information as possible at this time, it is important to note that contributions to this research extend far beyond what is detailed here.

Biography :

Raghavender Maddali is a data engineering and quality assurance expert passionate about designing clean, scalable systems that drive clarity, confidence, and long-term value. With a career built at the intersection of engineering and strategy, he has led transformative projects across industries such as real estate and bankingâ??modernizing data platforms, streamlining QA processes, and developing automation frameworks that reduce complexity and improve efficiency. Proficient in cloud-native tools like AWS, Snowflake, and dbt, Raghavender builds resilient architectures that prioritize governance, adaptability, and simplicity. He views quality not as an afterthought, but as a core architectural principle. Beyond his technical work, Raghavender has authored over 20 articles on data engineering, AI, QA, and automation. He contributes as a peer reviewer and judge in leading innovation platforms and is an active member of IEEE, ACM, INFORMS, and DAMA. Driven by purpose and thoughtful design, he is committed to creating systems that grow, evolve, and quietly strengthen the world around them.