Varun Joshi

Senior Data Engineer at AWS

October 28-30, 2026Oakland University, Rochester, MI

About Varun

Highly motivated and results-oriented Data Engineer with 12+ years of experience in designing, building,and optimizing scalable data pipelines and architectures.Proven expertise in data warehousing, ETL/ELT processes, and cloud platforms. Passionate about leveraging Artificial Intelligence (AI) and Machine Learning (ML).

Designed and deployed AI-driven Data solutions, integrating LLM-powered coding assistants into Data Engineering to produce AI solutions for customers. Focused on leveraging LLMs and advanced engineering to build scalable, secure, and trustworthy platforms, resulting in significant efficiency gains, reduced on-call burden,and improved customer trust.

Driving AI adoption across teams to enhance productivity, streamline deployments, and improve end-user experience.

Links

linkedin blog company website

Conference Sessions

Conference SessionIntermediate60 min

RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece

RAG is only as good as the metadata behind it. Large language models are only as useful as the context you give them. "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece" keeps the conversation practical as Varun Joshi works through where schemas, lineage, query history, and dbt models fit in data engineering workflows.

Topic: Artificial Intelligence and Machine Learning
Time: Oct 29, 2026, 10:00 AM
Room: Room A

Conference SessionIntermediate60 min

Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation

Pipeline failures should not wait for someone to notice a bad report. Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. "Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation" turns that scenario into a session where Varun Joshi walks through agentic patterns for monitoring, root cause analysis, and auto-remediation.

Topic: Artificial Intelligence and Machine Learning
Time: Oct 30, 2026, 2:30 PM
Room: Room F