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Nagarro · Guadalajara, Jalisco, Mexico

Senior Staff Engineer - Data Engineer

directorfull timePosted 9 days ago
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Stack mentioned

data-engineeringdbtsqldata-governancesnowflakedatabricksbigqueryredshiftanthropiccopilotllmagentic-ai

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

- 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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