Data Engineer , Carpool Latam
Listed on 2026-03-01
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IT/Tech
Data Engineer, Data Analyst, Data Science Manager, Data Warehousing
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The LATAM Data Engineer is a technical contributor responsible for building and maintaining data pipelines, AI‑ready data warehouses, and scalable infrastructure that powers GenAI reasoning agents and BI solutions across Amazon’s Mexico and Brazil Retail operations. This role combines traditional data engineering with AI agentic frameworks, enabling Retail stakeholders in MX and to access accurate, real‑time data through both traditional dashboards and conversational AI interfaces.
The team is Amazon LATAM’s data and AI center of excellence, serving internal users across Mexico and Brazil. We build the infrastructure that powers our GenAI backend—a general‑purpose reasoning engine that connects to any data source and enables rapid deployment of AI solutions.
Key job responsibilitiesBuild and maintain the common metric warehouse that serves as the single source of truth for all GenAI reasoning agents. This ensures every agent consults consistent, accurate data when analyzing business metrics, generating hypotheses, and validating insights.
Design and implement scalable ETL/ELT pipelines that power both traditional BI and AI solutions.
Enable reasoning agentic solutions by building robust data foundations that support
- Document Analysis Workflows:
Pipelines feeding multi‑phase workflows where agents read, analyze, generate hypotheses, and validate against real data - Financial Data Integration: P&L account data with historical patterns for automated analysis and hypothesis generation
- Selection Analysis Data:
Funnel and discoverability metrics at product and buying‑situation granularity for automated decision support
- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, Spark
SQL, Scala) - Experience with one or more scripting language (e.g., Python, Korn Shell)
- Experience with big data technologies such as:
Hadoop, Hive, Spark, EMR - Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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