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Data Engineering Summit 2026 conference with professionals networking.

MAY. 14-15 2026
Hotel Radisson Blu, ORR /
Bangalore

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Anupam Pratap Singh

Head of Data Engineering at Porter

A Data & AI Platform leader with over a decade of experience in data engineering and architecture, currently leading the Data & AI Platform at Porter on GCP. They drive initiatives across real-time analytics, experimentation, AI tooling, and self-serve data infrastructure, with a proven track record of building scalable data platforms, delivering commercial data products, and leading high-performing teams.

Their expertise includes AI and LLM engineering (Claude, Vertex AI, Hugging Face), agentic AI workflows, and generative AI techniques such as RAG pipelines and embeddings. They bring strong capabilities in batch and streaming systems (Apache Spark, Kafka), modern data warehousing (BigQuery, Snowflake, Redshift), and cloud platforms (GCP, AWS), along with experience in orchestration (Airflow, dbt), DevOps (Docker, Kubernetes, Terraform), and BI tools like Tableau and Superset, complemented by a solid foundation in data governance and observability.

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All Sessions by Anupam Pratap Singh

  • Day 1 | Hall 1 - Thought Leadership and Strategic Insights


  • 14:20  - 14:40
    As organizations rush to layer AI on top of their data warehouses, most are discovering an uncomfortable truth. AI-driven analytics is only as trustworthy as the metadata underneath it. Without lineage, ownership, and semantic context, even the best models confidently produce wrong answers, and no one can tell until a dashboard lies to an executive. In this talk, I'll share how we built a data catalog that serves as the backbone of our AI-driven analysis infrastructure. We'll walk through the pillars that make it work: end-to-end lineage from source systems to dashboards, blast-radius analysis that prevents silent breakage, documentation and accountability that AI agents can actually reason over, and a metrics layer that turns all of this into reliable, explainable answers. You'll leave with a concrete blueprint for why catalogs aren't documentation tools, they're infrastructure, and why getting this foundation right is the difference between AI that hallucinates and AI that works.