Senior Staff Engineer - Data Engineer

Nagarro; Región Centro; Presencial; MXN 1.000.000 - 1.300.000; Jornada completa

Descripción de la vacante

Nagarro is seeking an experienced Data Engineer to advance our analytics and data infrastructure. You will design and implement dbt-based pipelines, optimize transformations, and ensure data quality across Snowflake, Databricks, BigQuery, or Redshift.

You will actively integrate Claude Code and GitHub Copilot into development workflows and help set AI governance standards. Ideal candidates have 8+ years in data engineering, strong SQL, dbt mastery, and experience with cloud data platforms.

Formación

• 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, or Amazon Redshift
• Daily, fluent use of Claude Code and GitHub Copilot for implementation, refactoring, test generation, and code review
• Ability to establish team standards for AI-assisted development: prompting, trust-vs-verify, 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 automation tooling

Responsabilidades

• Build and maintain robust data pipelines using dbt and cloud data platforms.
• Design and implement dbt models, materializations, and tests with data quality in mind.
• Collaborate with teams to integrate AI tooling into development workflows and CI steps.
• Establish and enforce standards for AI-assisted development and security.
• Document data lineage and maintain artifacts for governance.

Conocimientos

DBT expertise

SQL

Data transformation

AI-assisted development

Prompt engineering

Herramientas

dbt Core/Cloud

Snowflake

Databricks

Google BigQuery

Amazon Redshift

Claude Code

GitHub Copilot

Descripción del empleo

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 ofClaude Codeand/orGitHub Copilotfor 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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