Lead Data Systems Engineer, AI-Driven Data Products

Salesforce; Ciudad de México; Híbrido; MXN 1.200.000 - 1.800.000; Jornada completa

Descripción de la vacante

Salesforce in Mexico City is seeking a Lead Data Systems Engineer to own end-to-end data product delivery. You will build, maintain, and enhance production-grade data solutions, leveraging AI agents and domain knowledge to ensure quality and governance across a complex stack including Snowflake, Salesforce, and AWS.

We expect hands-on expertise in Python, SQL, and modern data tooling (dbt, Airflow, Spark). The role emphasizes architecture, experimentation, and scalable, secure data workflows in

Formación

• 5+ years in high-stakes Data Engineering, Architecture, or Data Science.
• Strong Python and SQL expertise.
• History of using generative AI to accelerate output.
• Ability to manage end-to-end data strategy with minimal supervision.
• Superior ability to identify logic errors in agentic outputs.

Responsabilidades

• Architect and maintain a private ecosystem of 10+ autonomous agents for ETL, synthetic data, QA, and modeling.
• Design multi-step reasoning architectures and verification protocols for autonomous validation.
• Transform high-level requirements into production-grade data products independently.
• Govern data pipelines with governance-as-code principles.
• Develop and maintain MCP servers to access Snowflake, Salesforce, AWS, and internal data catalogs.

Conocimientos

Python

SQL

Airflow

dbt

Spark

Snowflake

AI/agentic development

Prompt engineering

Educación

Bachelor's degree in CS/Engineering

Herramientas

Docker

Kubernetes

Serverless

LangGraph

Descripción del empleo

Salesforce in Mexico City is seeking a Lead Data Systems Engineer to own end-to-end data product delivery. You will build, maintain, and enhance production-grade data solutions, leveraging AI agents and domain knowledge to ensure quality and governance across a complex stack including Snowflake, Salesforce, and AWS.

We expect hands-on expertise in Python, SQL, and modern data tooling (dbt, Airflow, Spark). The role emphasizes architecture, experimentation, and scalable, secure data workflows in