AI Agent Data Engineer Snowflake

Remote • Posted 1 hour ago • Updated 1 hour ago
Full Time
Remote
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Artificial Intelligence
  • ELT
  • Extract
  • Transform
  • Load
  • RBAC
  • Meta-data Management
  • Semantics
  • Slack
  • Salesforce.com
  • AI Agent
  • MCP
  • DMCP
  • Snowflake
  • Claude
  • agent data

Summary

We are seeking a hands-on AI Agent Data Engineer Snowflake with strong experience in DMCP and MCP protocols, Snowflake, and Claude to build endtoend AI agents and enterprise integrations. This role centers around designing semantic models for Slack, ingesting Salesforce customer chat data, and building agent workflows that rely on Snowflake as the core data and feature platform.

The engineer will also be responsible for building the required infrastructure, setting up RBAC, and developing secure, production-grade integrations that use Snowflake output data to power Claude-based agents.

Title: AI Agent Data Engineer Snowflake

Role Type: 3-6 months ContracttoHire

Location: 100% Remote

Focus: Endtoend Agent Development + Integrations + Snowflakecentric AI Infrastructure

Key Responsibilities

AI Agent Development (EndtoEnd)

  • Build production-grade AI agents using Claude, Snowflake data outputs, and MCP/DMCP protocol integrations.
  • Design agent workflows that consume Slack semantic models and Salesforce chat outputs.
  • Implement retrieval, context assembly, and agent orchestration pipelines.

Protocol-Based Integrations (MCP / DMCP)

  • Build and maintain MCP protocol integrations between Slack and Claude.
  • Implement DMCP-based Snowflake Claude integrations for agent data access.
  • Ensure secure, reliable, and scalable protocol communication across systems.

Snowflake-Centric Data Engineering

  • Ingest and model Salesforce customer chat data into Snowflake.
  • Build semantic layers, feature tables, and agent-ready datasets.
  • Develop ELT/ETL pipelines using Snowflake Streams, Tasks, Snowpipe, or dbt.

Slack Semantic Modeling

  • Build semantic models for Slack conversations, channels, and message metadata.
  • Structure Slack data for agent reasoning, retrieval, and workflow triggers.

Infrastructure & RBAC

  • Stand up development and production environments for agent workloads.
  • Implement RBAC, secrets management, and secure service-to-service communication.
  • Build monitoring, logging, and observability for all integration services

We are an Equal Opportunity Employer

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: RTX1714f1
  • Position Id: AI-DTX 08
  • Posted 1 hour ago
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