Staff Engineer - Data Engineer
Nagarro Guadalajara, Sonora, Mexico
IT Services and IT Consulting · 10,001+ employees
About the role
Develop complex data transformations and maintain production-grade data pipelines using dbt. Establish team standards for AI-assisted development and integrate AI tooling into developer workflows.
What they look for
Requirements
Requires 6+ years of experience in data engineering and 3+ years of hands-on experience with dbt. Candidates must possess strong SQL expertise and proficiency in cloud data platforms like Snowflake, Databricks, or BigQuery.
Full description
Company 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!
Job Description
- 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
- Service Region: UCC
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