Lead Agentic Data Systems Engineer

Salesforce; Ciudad de México; Presencial; MXN 900.000 - 1.200.000; Jornada completa

Descripción de la vacante

Salesforce is hiring a Lead Data Systems Engineer in Mexico City for a hybrid position. The role involves architecting data systems and managing a team of autonomous agents. Ideal candidates should have over 5 years of experience in Data Engineering, a strong knowledge of Python and SQL, and familiarity with AI environments. This position offers the opportunity to lead innovative data product development while collaborating with various stakeholders across the organization.

Formación

• Strong Python / SQL expertise.
• Documented history of using generative AI.
• Ability to manage end‑to‑end data strategy with minimal supervision.

Responsabilidades

• Architect and maintain ecosystems of autonomous agents.
• Design verification protocols for agent outputs.
• Transform business requirements into data products.

Conocimientos

Production‑grade proficiency in Python

Fluency in AI‑native development environments

Expert‑level knowledge of chain‑of‑thought prompting

Understanding of Salesforce Core and Data 360

Experience using agentic workloads via Docker and Kubernetes

Educación

5+ years of experience in Data Engineering

Herramientas

dbt

Airflow

Apache Spark

Snowflake

Descripción del empleo

Lead Data Systems Engineer

Mexico City | Full-Time • Hybrid

Department Overview

The Enterprise Data & AI Solutions group is the organization's strategic hub for cognitive automation. We move beyond traditional data management to build the autonomous engines that power executive decision‑making. Our team is composed of Architects of Autonomy, professionals with the technical depth to build systems from the ground up and the strategic vision to leverage AI to ensure scalability. We partner with the C‑suite to solve high‑complexity challenges by deploying sophisticated multi‑agent ecosystems that operate with continuous uptime.

Role Description: The Builder

We are seeking a Lead Agentic Data Systems Engineer – a hands‑on, depth‑first engineer who will turn architectural blueprints into hardened, production‑grade data products. This role is defined by execution depth. You will own the product end‑to‑end – building, maintaining, enhancing it, and constructively challenging the design when implementation reality demands it. You will lead the redesign of the data team model, replacing human personnel management with complex "hand‑off" protocols between specialized AI agents, acting as the central anchor for a hybrid human‑agent intelligence unit.

Core 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, and contextual agent 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.

Technical Profile

• Production‑grade proficiency in Python, dbt, Airflow, advanced SQL, Apache Spark, and Snowflake.
• Fluency in AI‑native development environments (e.g., Cursor, Codex, Claude Code); expert in prompt engineering; mastery of agentic frameworks such as LangGraph; leverage MCP servers to retrieve data from the 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), Event‑Driven Architectures, semantic layer, and knowledge graphs.
• Experience using agentic workloads via Docker, Kubernetes, and serverless compute environments.

Qualifications

• 5+ years of experience in high‑stakes Data Engineering, Architecture, or Data Science.
• Strong Python / SQL expertise.
• Documented history of using generative AI to accelerate personal and departmental output by orders of magnitude.
• 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.

A Day in the Life: Operational Workflow

08:00 – Intelligence Synthesis: Scout agents audit overnight pipelines; you review anomalies, approve SQL remediation and deployment.

10:30 – Architectural Orchestration: You define parameters for a market volatility stress test, orchestrate agents to pull data, run simulations, and build an executive dashboard.

13:30 – Knowledge Retrieval & Documentation: An information retrieval agent parses Slack, Jira, GitHub to generate a technical summary and updates the metadata repository.

15:30 – Defensive Systems Engineering: You build red‑team agents to test logic and security, creating a self‑healing digital immune system.

18:00 – Asynchronous Task Deployment: You initiate an analytical task to analyze churn data for latent correlations; agents complete the work overnight.

Accommodations

If you need a reasonable accommodation during the application or recruiting process, please submit a request via the Accommodations Request Form.

Posting Statement

Salesforce is an equal‑opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. We believe in equality for all and aim to create an inclusive workplace free from discrimination. All hiring decisions are based on merit, competence, and qualifications without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to all aspects of employment, including recruiting, hiring, job assignment, compensation, promotion, benefits, training, and evaluation.