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

Data Engineers in Toronto January 2027 Semimonthly Meeting

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

Abstract:
Every data engineer knows the 2 AM pipeline failure, the one nobody notices until Friday's report is wrong.

In this session, we break down five AI agents that are changing how data teams operate: from monitoring pipelines 24/7 and catching schema drift at ingestion, to closing the gap between a production failure and its root cause in minutes. We'll walk through real implementation patterns, including a baseline-learning monitoring agent and a tool-use driven incident response loop, and discuss what the shift to agentic data engineering actually means for the way teams are built and how engineers grow. Whether you're evaluating agents for your platform or already running them in production, you'll leave with concrete patterns you can apply immediately.

Speaker: Varun Joshi, Senior Data Engineer at AWS

Speaker Profile:
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.

The meeting is over Microsoft Teams, and the joining link is https://vip.dataengineersintoronto.org/webinar

See you at the meeting!