Pmts - Principal Agentic Data Engineer

Salesforce Salesforce · Enterprise · Mexico City, Mexico

The Principal Agentic Data Systems Engineer will architect and maintain a private ecosystem of autonomous agents for ETL, synthetic data generation, automated QA, and predictive modeling. This role involves designing multi-step reasoning architectures and verification protocols for agent self-validation, acting as a Human-in-the-Loop Orchestrator to manage complex hand-offs between specialized AI agents, and ensuring governance for data pipelines and agent development. The engineer will also develop and maintain Model Context Protocol (MCP) servers for agent access to data sources.

What you'd actually do

  1. Architect and maintain a private ecosystem of 10+ autonomous agents specialized in ETL, synthetic data generation, automated QA, and predictive modeling.
  2. Design multi-step reasoning architectures and verification protocols to ensure agents autonomously validate and peer-review their own outputs.
  3. Transform high-level, ambiguous business requirements into production-ready data products independently, bypassing the need for mid-level project management.
  4. Use domain knowledge to ensure deployed tools are well governed. Governance as code for data pipelines and Agentic development. Context aware Agent development.
  5. Develop and maintain Model Context Protocol (MCP) servers to provide agents with secure, deep-link access to Snowflake, Salesforce, AWS, and proprietary internal data catalogs.

Skills

Required

  • Python
  • dbt
  • Airflow
  • advanced SQL
  • Apache Spark
  • Snowflake
  • Prompt Engineering
  • agentic frameworks (e.g., LangGraph)
  • chain-of-thought prompting
  • self-correction loops
  • iterative reasoning paths
  • Data Mesh
  • Data-as-a-Product (DaaP)
  • Event-Driven Architectures
  • Docker
  • Kubernetes
  • serverless compute environments
  • generative AI
  • end-to-end data strategy

Nice to have

  • Cursor
  • Codex
  • Claude Code
  • Salesforce Core
  • Data 360
  • Semantic layer
  • Knowledge Graphs

What the JD emphasized

  • mastered traditional data engineering
  • next frontier: Agentic Force Multiplication
  • Human-in-the-Loop (HITL) Orchestrator
  • high-level design and supervision of autonomous agents
  • manage complex "hand-off" protocols between specialized AI agents
  • production-grade proficiency
  • Fluency in AI-native development environments
  • Expert in Prompt Engineering
  • Mastery of agentic frameworks
  • Expert-level knowledge of chain-of-thought prompting, self-correction loops, and iterative reasoning paths
  • documented history of using generative AI to accelerate personal and departmental output by orders of magnitude
  • function as a "Domain Data Officer"
  • Superior analytical judgment

Other signals

  • autonomous agents
  • agentic orchestration
  • multi-agent ecosystems
  • human-in-the-loop orchestrator
  • governance as code