Sr Data Engineer (LATAM Remote)

UP.LabsGuadalajara,
Remote

About The Position

UPLabs is a dynamic venture studio dedicated to building innovative startup companies from the ground up. Our team thrives on solving complex problems, driving technological advancements, and creating impactful digital products. We’re seeking a highly skilled professional to join our growing team and contribute to our mission of launching the next wave of AI powered solutions for enterprise customers.

Requirements

  • Strong data engineering expertise, including designing and operating data pipelines, data models, and batch/stream processing workflows in a production environment.
  • Proficiency with Python for building data pipelines, automation, and data tooling.
  • Advanced SQL skills for data transformation, analysis, and performance tuning.
  • Experience working with PostgreSQL, including schema design, query optimization, and data integrity best practices.
  • Experience building data pipelines and workloads on Databricks.
  • Experience using dbt to develop, test, and maintain modular analytics engineering workflows.
  • Experience working with MongoDB or other document databases as part of modern data stacks.
  • Experience working with one or more major cloud platforms: AWS, Azure, or GCP.

Nice To Haves

  • Experience using Snowflake for cloud data warehousing, modeling, and analytics workloads.
  • Working knowledge of Apache Spark for large-scale data processing.

Responsibilities

  • Architect, build, and maintain scalable, production-grade data pipelines and data models to enable reliable ingestion, transformation, and delivery of data.
  • Own end-to-end pipeline reliability, including orchestration, monitoring, alerting, and incident response to meet data freshness and quality expectations.
  • Partner with product, engineering, and analytics stakeholders to define data requirements, translate them into clear technical specifications, and deliver iteratively.
  • Improve performance, scalability, and cost efficiency of data workloads by tuning SQL queries, optimizing storage/compute patterns, and standardizing best practices across projects.
  • Document data models, pipeline behavior, and operational runbooks to ensure maintainability and smooth knowledge transfer across accounts.
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