Data Engineer Senior | AI & Data

Senior Data Engineer | AI & DataDET TEAM
Mexico City
We are seeking a Senior Data Engineer to design, build, and optimize scalable data pipelines and models that power analytics, AI, and business insights. You will work across modern data platforms such as dbt, Snowflake, Informatica, Airflow. You will also use AI-native development tools and agentic workflows to build faster, validate more rigorously, and keep data products reliable.
Key Responsibilities
Data and AI engineering
Design, build, and maintain automated pipelines and dimensional/star/snowflake models using dbt, Snowflake, Informatica, and Airflow. These power analytics, reporting, and ML/AI use cases.
Integrate data from multiple systems, including Salesforce Core and Data 360, into unified, high-quality datasets.
Ensure accuracy, completeness, and consistency through validation, observability, and automated monitoring. Troubleshoot issues and optimize for performance, scalability, and cost.
Partner with analysts, product managers, and business teams to turn ambiguous requirements into production-ready data products.
Use AI-native tools (e.g., Cursor, Codex, Claude Code) to accelerate development, testing, documentation, and troubleshooting.
Build agentic workflows for ETL, automated QA, anomaly detection, and pipeline remediation. Agents propose fixes, and you review and approve them.
Design verification patterns (self-correction loops, peer-review agents, human-in-the-loop checkpoints) so agent output is validated before production.
Develop MCP servers that give agents secure, governed access to Snowflake, Salesforce, and internal data catalogs. Use agents to keep documentation, lineage, and metadata current.
Supervise long-running agent tasks with checkpoints, cost controls, and audit trails. Contribute to agent-assisted modeling and simulation workflows, reviewing assumptions and uncertainty before decisions are acted on.
Governance, quality, and security
Apply governance as code to pipelines and agents: lineage, access controls, semantic definitions, and audit logging.
Support data quality and governance initiatives in line with standards and best practices.
Contribute to red-team and validation agents that probe pipelines and agents for logic flaws, vulnerabilities, and integrity risks.
Team and craft
Lead and mentor peers on AI-assisted engineering practices.
Improve tools, standards, and documentation, and evaluate emerging data and AI technologies with sound judgment.
Required Skills and Experience
5–7 years of hands-on data engineering experience.
Strong data modeling skills (dimensional, star/snowflake) and data curation experience. Production experience with dbt and Snowflake. Working knowledge of Informatica and Airflow.
Proven use of AI coding assistants and generative AI to deliver measurably faster, with examples.
Hands-on context engineering and agent design (tool use, reasoning, self-correction).
Sharp judgment to catch logic errors and hallucinations in AI-generated output before it reaches production.
Strong problem-solving, attention to detail, and cross-functional communication.
Preferred Skills (Must)
Agentic frameworks and building or consuming MCP servers.
Salesforce Core and Data 360 knowledge.
Data Mesh, Data-as-a-Product, event-driven architecture, semantic layers, and knowledge graphs.
AI evaluation, guardrails, and adversarial testing of agents.
Statistical and simulation literacy, and experience orchestrating long-running agent workloads.

Employment type
Full time