Principal Agentic Data Systems Engineer

Salesforce Salesforce · Enterprise · San Francisco, CA

This role focuses on architecting and managing a private ecosystem of autonomous AI agents for data engineering tasks (ETL, synthetic data generation, QA, predictive modeling). The engineer will design multi-step reasoning architectures, verification protocols, and integration with data sources, acting as a human-in-the-loop orchestrator for a digital workforce.

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
  • LangGraph
  • chain-of-thought prompting
  • self-correction loops
  • iterative reasoning paths
  • Salesforce Core and Data 360 understanding
  • Data Mesh
  • Data-as-a-Product (DaaP)
  • Event-Driven Architectures
  • Semantic layer
  • Knowledge Graphs
  • Docker
  • Kubernetes
  • serverless compute environments
  • generative AI to accelerate output

Nice to have

  • Cursor
  • Codex
  • Claude Code

What the JD emphasized

  • 7+ years of experience in high-stakes Data Engineering, Architecture, or Data Science
  • documented history of using generative AI to accelerate personal and departmental output by orders of magnitude
  • The ability to function as a "Domain Data Officer," managing end-to-end data strategy for a business unit with minimal supervision.

Other signals

  • Designing and supervising autonomous agents
  • Managing hand-off protocols between specialized AI agents
  • Transforming business requirements into production-ready data products using AI agents