Srinivasa Rao M
Data Engineer & Founder @DeepTrics
Meridian, Idaho, United States
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Srinivasa Rao is a Data Engineer and Platform Lead with over 10 years of experience designing and modernizing large-scale data systems. He has led complex public sector data transformation initiatives involving over 100 million records across decades of legacy systems, migrating them into modern cloud-based architectures using Azure and Microsoft technologies.
His work focuses on solving real-world data challenges — from fragmented legacy environments to building scalable, validated data platforms that support modern applications and AI use cases.
Srinivas is also the founder of DeepTrics, an initiative focused on bridging the gap between academic learning and real-world software development by providing hands-on industry experience to students.
He actively shares insights on data engineering, AI readiness, and system design, helping professionals understand the true foundation behind intelligent systems.
Area of Expertise
Topics
Stop Building AI on Bad Data: Engineering Reliable Data Foundations
Generative AI is transforming every industry, but no prompt can compensate for unreliable data.
This session explores why data engineering remains the most important investment for successful AI initiatives. Through practical examples, we'll examine how poor metadata, inconsistent schemas, duplicate records, missing relationships, and weak governance directly impact AI outcomes.
Instead of focusing on models, we'll focus on the engineering practices that enable AI systems to deliver trustworthy and measurable results.
What you'll learn
Why data quality determines AI success
Engineering practices for trustworthy AI
Data governance essentials
Measuring AI readiness
Practical implementation roadmap
Why AI Fails Before the Model
Everyone is talking about models, agents, and copilots.
Yet many AI initiatives struggle long before a prompt is ever written or a model is ever deployed.
The reality is that AI systems inherit the strengths—and weaknesses—of the data ecosystems beneath them. Fragmented data, undocumented business rules, inconsistent metadata, disconnected systems, and poor feedback mechanisms often become the true barriers to AI success.
While leading large-scale modernization efforts involving more than 100 million records across legacy databases, documents, spreadsheets, and operational systems, I discovered that the hardest AI challenges were not AI challenges at all. They were data challenges.
In this session, I'll share practical lessons from transforming decades-old systems into modern, AI-ready platforms. We'll explore the hidden engineering work that happens before machine learning, retrieval-augmented generation (RAG), agents, and decision intelligence can deliver value. Attendees will learn how to identify foundational risks, reconstruct critical context from legacy systems, establish trustworthy data pipelines, and design feedback mechanisms that enable AI systems to improve over time.
If you're building AI applications, platforms, or agents, this session will help you understand why successful AI starts long before the model—and how to build the foundation that allows AI to succeed.
From Legacy SQL to AI-Ready Data: Lessons from Migrating 100 Million Government Records
Modern AI depends on trustworthy data, but many organizations still rely on decades-old databases filled with undocumented relationships, duplicate records, and inconsistent business rules.
Drawing from a real-world government modernization project, this session walks through the complete migration journey—from reverse engineering legacy SQL databases to building reliable cloud-ready data platforms.
We'll discuss schema reconstruction, business rule discovery, staging strategies, validation frameworks, reconciliation techniques, and production cutover planning. You'll also learn how these engineering practices create the foundation for future AI initiatives.
Whether you're modernizing legacy applications or preparing data for analytics and AI, this session offers practical techniques you can apply immediately.
What you'll learn
Reverse engineering legacy databases
Designing migration pipelines
Data validation strategies
Production cutover planning
Preparing SQL environments for AI
The Importance of Data in the Era of AI: Why Data Makes AI Real
Artificial Intelligence is often seen as a breakthrough in models and algorithms. But in reality, AI became practical and impactful only because of one thing: data — and the infrastructure that supports it.
Although the concept of AI has existed since the 1950s, it is only in recent years — with the rise of cloud computing, scalable storage, and modern data platforms — that AI has become truly usable at scale. Without data, AI remains just a theory.
In real-world systems, data is often messy, fragmented, and undocumented — yet we expect AI to make sense of it.
In this session, we will explore why data is the true foundation of every AI system. While most discussions focus on tools, frameworks, and models, the real challenges lie in understanding, preparing, and managing data at scale.
Through real-world experiences working with decades of legacy systems, we will uncover the hidden complexities of data — including inconsistent formats, missing relationships, duplicate records, and lack of data ownership.
We will also connect these challenges to modern AI systems and show how data pipelines, validation strategies, and infrastructure design directly impact the success of AI initiatives.
This session provides a practical perspective on how data flows through real systems, where it breaks, and how to build a strong foundation before applying AI.
What you will learn:
Why AI success depends more on data than models
How modern infrastructure made AI practical
Common data challenges in real-world systems
A practical approach to preparing data for AI
How to think about data as a long-term asset, not just input
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