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Ayan Putatunda
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Ayan Putatunda

Staff Data Engineer. Data platforms for AI, and the agentic systems on top of them.

Pleasanton, CA · ayanputatunda87@gmail.com · ayanputatunda.com · linkedin.com/in/ayanputatunda · github.com/AyanPutatunda

Summary

Staff Data Engineer with sixteen years architecting petabyte-scale data platforms that power production AI. Currently leads AI-readiness for the Zendesk Data Platform: context governance, Model Context Protocol architectures that let agents consume governed enterprise data, and agentic workflows that automate the analytics-engineering lifecycle end to end with a human at every review gate. Keynoted IEEE AIC 2026 and co-authored a vision paper under review at IEEE BigData 2026. Builds and ships full-stack AI products independently at Axiomic AI.

Core skills

Agentic AI
Anthropic Claude API, Claude Code, Claude Agent SDK, MCP, LangGraph, Multi-agent systems, RAG and vector search, LLM evaluation
Data platform
Snowflake, Snowflake Cortex, dbt, Airflow, Fivetran, BigQuery, Spark, Kimball modelling, Semantic layers
Languages and cloud
Python, SQL, Docker, GitHub Actions, GCP, Snowflake Data Cloud
Leadership
Technical mentoring, Team building, Stakeholder management, Engineering best practices

Experience

Staff Data EngineerNov 2024 – present
Zendesk, San Francisco
  • Lead AI-readiness work for the Zendesk Data Platform: Snowflake Cortex semantic layers and skills-driven agentic data engineering.
  • Architected petabyte-scale infrastructure with Fivetran, Airflow, and dbt; 40% better delivery efficiency at 99.9% uptime across 5+ engineering teams.
  • Established engineering best practices and mentor engineers on multi-system, long-term projects.
Staff Data EngineerMay 2022 – Nov 2024
Achieve (formerly Freedom Financial Network), San Mateo
  • Led the FDR Sales Data Transformation on Google Cloud: 500K+ unified customer profiles in a BigQuery data lake.
  • Batch and streaming ETL frameworks reduced data inconsistencies by 60%.
Engineering Manager, Principal Data Engineer, Senior Data EngineerSep 2019 – Apr 2022
Noodle.ai, Palo Alto and San Francisco
  • Architected the pipeline for a first-in-class supply chain AI platform; TB-scale, 99.9% uptime.
  • Built and scaled the data engineering team from 2 to 8 with 100% retention.
  • Configurable models and a reusable Airflow framework: implementation time down 50%, development up 60%.
Team Lead, Associate Projects, Senior Developer, Programmer AnalystOct 2010 – Sep 2019
Cognizant, Kolkata / Buenos Aires / San Francisco
  • Multi-channel retail POS data integration (RAYMARK, MSBI, SQL Server) for global retail clients; 99.5% data accuracy with fault-tolerant ETL.
  • Automated reconciliation processes eliminated manual validation and reduced errors by 85%.

Selected work

Skills-driven agentic data engineering2025–2026
Zendesk Data Platform
  • A governed library of AI skills and sub-agents that automates the analytics-engineering lifecycle across multi-repo dbt and ingestion workflows, with humans in the loop.
  • Roughly 2.5× faster time-to-production for analytics work, as presented at dbt Summit 2026.
  • Targeting an 80% reduction in manual pipeline development across multi-repository dbt and ingestion workflows.
Autonomous incident response agent2025
Zendesk Data Platform
  • Zero-touch detection, root-cause analysis, and auto-remediation of data pipeline failures using the Claude Agent SDK with Snowflake, dbt, and GitHub MCP servers.
  • Targeting a 70% reduction in mean time to resolution for pipeline incidents.
  • On-call load shifted from log reading to reviewing a proposed fix.
Semantic layer on Snowflake2025–2026
Zendesk Data Platform
  • A unified business vocabulary across 10+ domains, built with dbt metrics and Snowflake Cortex semantic views, so people and agents ask questions in the same words and get the same answer.
  • Ad-hoc query requests down 45%.
  • Self-serve analytics opened to non-technical stakeholders.
contextctl: auditable context infrastructure for AI agents2026
Research, with Suhas Jangoan
  • A vision paper and open-source prototype arguing that agent context should be governed data infrastructure: open files for truth, Git for time, a hash-named evidence folder, one SQLite sidecar for speed, and a token-budgeted serving protocol. Under review at IEEE BigData 2026.
  • Vision paper submitted to the 1st IEEE Big Data Workshop on Agentic AI for Big Data (AAI-BD 2026), IEEE BigData 2026. Decision expected October 2026.
  • Measured feasibility of the primitives: snapshot commit 155 ms and full sidecar rebuild 0.47 s at 10,000 concepts; point-in-time reads and reverts around 3 to 5 ms; 28 MB total storage.
Axiomic AI agent harness2025–2026
Open source, personal R&D
  • The multi-agent repository template every Axiomic AI product is built from: agent prompts, MCP server configs, CI, and deploy wiring. Fork, describe the domain, ship.
  • PersonasFlow, EstateVision, and WealthPilot all run on it; each new product started from the same fork.
  • Axiomic AI operates as an experimental enterprise: the harness is the company's engineering team.

Research and publications

  • Vision Paper: contextctl — Auditable, Open-Source Context Infrastructure for AI Agents. Ayan Putatunda, Suhas Jangoan. 1st IEEE Big Data Workshop on Agentic AI for Big Data (AAI-BD 2026), IEEE BigData 2026. Under review, decision expected October 2026.
  • Essential PySpark for Scalable Data Analytics. Technical reviewer. Packt Publishing, ISBN 978-1-800563094. Published

Speaking

  • Session, Applied AI Summit, Oct 13–15, 2026. Making a Data Platform AI-Ready.
  • Session, dbt Summit 2026, Las Vegas, Sep 15–18, 2026. AI-Powered Data Development: How Agentic SDLC & dbt-MCP Transformed Our Data Engineering Workflow, with Suhas Jangoan.
  • Keynote, IEEE 5th World Conference on Applied Intelligence and Computing (AIC 2026), Aug 29, 2026. Unlocking Intelligence through Data.
  • Address, University of San Francisco. Guest address to the graduating class, MS in Data Science & AI.
  • Podcast, #idataengineer podcast, Feb 5, 2021. Data Engineering Confessions #9.

Community

  • Judge, 2nd NextGen Hackathon 2026. ACM Fremont Chapter with the Soft Computing Research Society, Aug 15–16, 2026.
  • Mentor, Junior and mid-level engineers moving into data and AI engineering, US and Singapore. 20–30 professionals mentored across enterprises; public live sessions on YouTube, Ongoing.
  • Technical interviewer, 50+ technical interviews as a coding assessor. Across enterprises, Ongoing.
  • IEEE, ieee senior member (application in progress).

Independent work and teaching

  • Axiomic AI (axiomic-ai.com). An experimental enterprise: one person, one agent harness, a portfolio of shipped products. Live products: PersonasFlow, EstateVision, WealthPilot, each built on a shared open-source agent harness.
  • Built from Bits (YouTube @ayan-in-tech). Hands-on data and AI tutorials: Claude Code, MCP, agents, dbt, Snowflake. Also on Substack and Medium.
  • The Practitioners Pod (YouTube @thepractitionerspod). Conversations with people who ship data and AI systems for a living.
  • Built from Bits, learning platform. Under construction.

Education

  • Master of Computer Science in Data Science, University of Illinois Urbana-Champaign
  • B.Tech in Computer Science and Engineering, West Bengal Institute of Technology