Staff Engineer - Data Engineer
At a glance
- Salary
- Not published
- Location
- Guadalajara, Mexico
- Work type
- Hybrid
- Level
- Staff
- Posted
- today
- Verified live
- today
- Experience
- 6+ years
- Skills
- MCP
- Filed under
- AI Agents
How the pay compares
This posting doesn't publish pay. 155 of the 262 Staff AI Agents roles worldwide on this board do: the middle half pay $220k–$287k, with a median of $250k. Too few roles in Mexico publish pay for a local comparison, so this is every country together — mostly US pay.
Middle half of the 155 that publish payMedian10th–90th percentileAnnual, USD
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Job description
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!
- 6+ 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, including
- dbt models and materialization
- Incremental models
- Macros and Jinja
- dbt tests and data quality frameworks
- Snapshots
- Seeds and sources
- Documentation and lineage
- dbt packages
- Strong experience with at least one cloud data platform, such as:
- Snowflake
- Databricks
- BigQuery
- Amazon Redshif
AI skills (required for all roles)
- 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