AI is reshaping industries, and data engineering is no exception. In this interview, we sit down with Deepak Yadav, Head of Data Engineering and AI/ML at Amazon, and speaker at the Data Innovation Summit 2025!
With 18 years of experience, Deepak offers a front-row seat to the evolution of AI in data engineering. He shares his insights on AI-driven platforms, cross-team collaboration, the challenges of implementing AI, and how it’s transforming financial operations, scalability, and data quality worldwide. Dive in for a glimpse into the future of AI in data engineering!
Hyperight: Deepak, with over 18 years of experience in data engineering and data science, you’ve seen the industry evolve significantly. Can you share your professional journey and what excites you most about your work?
Deepak Yadav: I’ve had a dynamic journey in data engineering and data science, starting with traditional data warehousing and evolving into AI/ML-driven automation. Over the past 18+ years, I’ve worked across financial services, retail, and marketing, building scalable, low-latency data platforms and driving AI/ML adoption.
In my current role at Amazon, I lead the Data Engineering and AI/ML division in Fintech Automation Services, where I focus on transforming financial data operations. I’m particularly passionate about leveraging AI/ML for automation, anomaly detection, and predictive analytics to improve financial decision-making. Building intelligent, real-time data processing solutions and enhancing data governance excite me, as they directly impact business efficiency and strategic growth.
Hyperight: In your talk at the Data Innovation Summit 2025, you’ll explore AI’s impact on shaping modern platforms. What can the delegates expect from your presentation?
Deepak Yadav: Delegates at the Data Innovation Summit 2025 can expect an insightful session on how AI is transforming modern data platforms, particularly in financial automation and analytics. I’ll dive into real-world use cases, including how AI-driven data engineering optimizes financial reporting, enhances decision-making, and automates complex processes at scale.
Hyperight: In your talk, you will mention the importance of collaboration between data engineers, data scientists, and MLOps. What are some practical ways these teams can work together more effectively to leverage AI’s potential?
Deepak Yadav: For AI to work well, data engineers, data scientists, and MLOps teams need to collaborate effectively. They can do this by building a shared data platform where data is clean, accessible, and ready for AI models. Using a feature store helps teams reuse and standardize data for machine learning. Automating model deployment with CI/CD ensures that AI models are smoothly integrated into production. Regular team check-ins help everyone stay aligned on data quality, model accuracy, and system performance.
Hyperight: What are some significant challenges organizations face when implementing AI-driven data solutions, and how can they overcome them?
Deepak Yadav: Organizations face challenges in using AI-driven data solutions, but they can overcome them with the right steps. Poor data quality can be fixed with better data management and quality checks. Lack of AI skills can be solved by training teams and hiring experts. Scaling AI systems works best with cloud platforms and automation. Old systems can be connected to AI using APIs and gradual upgrades. Last but not the least, following rules and being transparent helps with legal and ethical concerns. With these steps, businesses can use AI effectively.
Hyperight: Data quality and scalability are two of the major benefits you will highlight in your talk. Can you share how AI enhances these aspects in modern data platforms?
Deepak Yadav: AI improves data quality by automating cleansing, anomaly detection, and validation, ensuring accuracy and reducing manual effort. It also enhances scalability through automated data pipelines, predictive scaling, and real-time processing, enabling efficient handling of large datasets. With AI-powered MLOps, model deployment and monitoring become seamless, making data platforms more agile and future-ready.
Hyperight: How do you see the role of MLOps evolving in the context of automating the data journey, and why is it crucial for AI-driven platforms?
Deepak Yadav: MLOps is key to automating AI-driven platforms by streamlining model deployment, monitoring, and updates. It ensures scalability, reliability, and compliance while integrating data engineering, DevOps, and AI workflows. By automating pipelines and governance, MLOps keeps AI models accurate, efficient, and adaptable at scale.
Hyperight: You’ve been in this field for over 18 years. How have you seen the evolution of AI in data engineering, and what do you expect the next decade to look like in this space?
Deepak Yadav: Over the years, I’ve seen AI transform data engineering from manual processes to real-time, intelligent automation. What once required extensive manual effort is now driven by AI-powered data quality, anomaly detection, and predictive analytics. Looking ahead, I expect AI-native data platforms to take over, enabling self-healing systems, automated governance, and GenAI-driven data transformation, making data engineering more autonomous, scalable, and business-driven.
Hyperight: What emerging trends in AI and data engineering do you think will have the most impact on the industry in the coming years?
Deepak Yadav: I see AI-driven automation streamlining data integration, pipeline orchestration, and governance, reducing manual effort. Real-time architectures will drive faster decision-making, while Generative AI will transform data transformation and query generation. AI-powered observability will enhance data quality and self-healing systems, ensuring reliability.
Don’t miss Deepak’s presentation at the Data Innovation Summit 2025! He’ll take a deep dive into automating the data journey, and how AI is shaping modern platforms. He’ll also explore AI-driven automation in financial operations, offering insights on improving data quality and scalability through AI-powered solutions. If you’re passionate about leveraging AI for business transformation and real-time data processing, this is a session you won’t want to miss!
Articles written by Deepak…
Transforming Data Engineering: The Role of AI in Quality, Efficiency, and Innovation. No longer just an enhancement, generative AI is fundamentally changing data engineering. It redefines how professionals process, analyze, and interact with data. AI’s impact is evident through industry events and corporate restructuring at companies like Snowflake and Databricks. However, its most significant influence is felt in the practical, day-to-day aspects of data engineering work.
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