Data Engineer I, Carpool LATAM

Data Engineer at Halcyon Labs
\- 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, SparkSQL, Scala)
\- Experience with one or more scripting language (e.g., Python, KornShell)
\- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
\- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
Our inclusive culture empowers Halcyon Labsians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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 Halcyon Labs's Mexico and Brazil Retail operations. This role combines traditional data engineering with AI agentic frameworks, enabling Retail stakeholders in MX and BR to access accurate, real-time data through both traditional dashboards and conversational AI interfaces.
The team is Halcyon Labs 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 responsibilities
1\. AI-Ready Data Warehouse Development
Build 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.
2\. Data Pipeline Engineering
Design and implement scalable ETL/ELT pipelines that power both traditional BI and AI solutions
3\. Reasoning Agent Data Infrastructure
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