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Senior Staff Engineer - Data Engineer

Salary
Not published
Location
Guadalajara, Mexico
Work type
Hybrid
Level
Staff
Posted
today
Verified live
today

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MCP

Filed underAI Agents

700 of 1517 AI Agents roles on this board publish pay; their median is $220k.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (15000+ experts across 26 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

  • 8+ years of experience in Data Engineering, Analytics Engineering, or related fields.
  • 3+ years of hands-on experience with dbt in production environments.
  • Strong expertise in SQL and complex data transformation development.
  • Strong understanding of dbt Core and/or dbt Cloud.
  • Experience with dbt models and materialization
  • Experience with incremental models
  • Experience building and maintaining macros using Jinja
  • Experience with dbt tests and data quality frameworks
  • Experience with snapshots
  • Experience with seeds and sources
  • Experience with documentation and data lineage
  • Experience using dbt packages
  • Strong experience with at least one cloud data platform:
  • Snowflake
  • Databricks
  • Google BigQuery
  • Amazon Redshift
  • Daily, fluent use of Claude Code and/or GitHub Copilot for implementation, refactoring, test generation, and code review
  • Ability to establish team standards for AI-assisted development: effective prompting, trust-vs-verify discipline on generated code, security/IP guardrails, and reviewing AI-authored changes
  • Working understanding of LLM fundamentals: context windows, tokens, model selection, and prompt/context engineering
  • Experience integrating AI into developer workflows and agentic/automation tooling (MCP servers, AI-driven CI steps, codegen and doc-generation pipelines)
  • Able to evaluate AI tooling pragmatically: measuring real productivity and quality impact, not hype

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