Principal Agentic Data Systems Engineer

Salesforce•San Francisco, CA

About The Position

Salesforce is seeking a Principal Agentic Data Systems Engineer to join the Enterprise Data & AI Solutions group. This role is for a technical visionary focused on Agentic Force Multiplication, shifting from traditional individual contribution to a Human-in-the-Loop (HITL) Orchestrator. The engineer will architect and supervise a high-performing digital workforce, acting as a "Team of One" to achieve the output of a 3-5 person engineering squad. The focus is on the high-level design and supervision of autonomous agents for engineering, QA, governance, and analytics workflows, rather than manual coding. This involves managing "hand-off" protocols between specialized AI agents and acting as the central anchor for a hybrid human-agent intelligence unit.

Requirements

Production-grade proficiency in Python, dbt, Airflow, and advanced SQL.
Expertise in Apache Spark, and Snowflake.
Fluency in AI-native development environments (e.g., Cursor, Codex, or Claude Code).
Expert in Prompt Engineering.
Mastery of agentic frameworks such as LangGraph.
Ability to leverage MCP servers to retrieve data from tool stack.
Expert-level knowledge of chain-of-thought prompting, self-correction loops, and iterative reasoning paths.
Understanding of Salesforce Core and Data 360.
Advanced understanding of Data Mesh, Data-as-a-Product (DaaP), and Event-Driven Architectures.
Knowledge of Semantic layer and Knowledge Graphs.
Experience using agentic workloads via Docker, Kubernetes, and serverless compute environments.
7+ years of experience in high-stakes Data Engineering, Architecture, or Data Science.
A 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.
Superior analytical judgment—the ability to identify subtle logic errors or hallucinations in agentic output before they reach production.

Responsibilities

Architect and maintain a private ecosystem of 10+ autonomous agents specialized in ETL, synthetic data generation, automated QA, and predictive modeling.
Design multi-step reasoning architectures and verification protocols to ensure agents autonomously validate and peer-review their own outputs.
Transform high-level, ambiguous business requirements into production-ready data products independently, bypassing the need for mid-level project management.
Use domain knowledge to ensure deployed tools are well governed, implementing governance as code for data pipelines and Agentic development.
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.