Lead Agentic Data Systems Engineer

About the role

Salesforce is hiring a Lead Agentic Data Systems Engineer in Mexico City, full-time and hybrid, within its Enterprise Data & AI Solutions group. The role turns the architectural blueprints set by Principal Engineers into production-grade data products, owning each product end-to-end. It focuses on deep, disciplined building and on orchestrating hand-offs between specialized AI agents rather than managing people.

Key responsibilities

• Architect and maintain a private ecosystem of 10+ autonomous agents for ETL, synthetic data generation, automated QA and predictive modeling.
• Design multi-step reasoning architectures and verification protocols so agents validate and peer-review their own outputs.
• Turn ambiguous business requirements into production-ready data products independently, without mid-level project management.
• Ensure deployed tools are well governed, with governance as code for data pipelines and agentic development.
• Develop and maintain Model Context Protocol (MCP) servers giving agents secure access to Snowflake, Salesforce, AWS and internal data catalogs.

Required profile

• 5+ 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.
• Ability to act as a Domain Data Officer, managing end-to-end data strategy for a business unit with minimal supervision.
• Strong analytical judgment to spot subtle logic errors or hallucinations in agentic output before production.

Required skills

• Production-grade Python, dbt, Airflow, advanced SQL, Apache Spark and Snowflake.
• AI-native development environments (Cursor, Codex or Claude Code) and prompt engineering.
• Agentic frameworks such as LangGraph and chain-of-thought prompting.
• Salesforce Core and Data 360, Data Mesh, Data-as-a-Product, event-driven architecture and knowledge graphs.
• Docker, Kubernetes and serverless compute.