This is a remote position.

We are looking for Data Engineers (Junior, Mid or Senior)!

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  Design, build, and maintain end-to-end data pipelines (batch and/or streaming), from ingestion to transformation and delivery.
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  Develop and operate ETL/ELT workflows, ensuring reliability, scalability, and performance.
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  Write efficient, production-grade SQL queries for data extraction, transformation, and analytics use cases.
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  Implement and maintain data models (e.g., star schemas, incremental models) optimized for analytics and reporting.
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  Develop reusable and modular Python code for data transformations and pipeline logic.
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  Monitor data pipelines, troubleshoot failures, and perform root cause analysis across code, orchestration, data sources, and cloud services.
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  Ensure data quality by implementing automated validation checks (schema validation, freshness checks, row-level assertions).
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  Translate business and analytical requirements into robust technical data solutions.
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  Collaborate with analysts, backend engineers, and other stakeholders to define data contracts and ensure data availability.
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  Actively participate in planning, estimation, and prioritization of data engineering tasks.
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  Proactively identify risks related to performance, scalability, or data integrity and propose mitigation strategies.
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  Contribute to continuous improvement of data platforms, processes, and team practices.
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  Write and maintain technical documentation for pipelines, schemas, and data lineage.
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  Communicate clearly with team members and clients, raising questions and concerns when requirements or priorities are unclear.
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  Support and mentor other team members when appropriate, contributing to overall team delivery.

Requirements

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  Professional experience as a Data Engineer working with production data pipelines.

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  Strong experience with SQL, including query optimization, indexing, partitioning, and performance trade-offs.
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  Professional experience writing Python for data transformations, following good design and modularization practices.
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  Experience designing and implementing data models for analytics use cases.
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  Experience building and operating pipelines using cloud-based data platforms.
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  Hands-on experience with Azure, Databricks, and Data Lake environments.
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  Experience operating data pipelines, including error handling, monitoring, and data quality processes.
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  Familiarity with Git for version control, including branching and resolving merge conflicts.
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  Experience working with Kubernetes or containerized data workloads.
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  Understanding of data formats such as Parquet or ORC, including cost and performance considerations.
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  Knowledge of basic data security and governance practices (access control, masking, PII handling).
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  Ability to deliver less complex tasks independently and more complex tasks with guidance.
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  Strong sense of ownership, responsibility, and accountability for data workflows.
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  Good organizational and time management skills, with the ability to estimate and meet delivery deadlines.
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  Advanced English for collaboration with global clients.
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  Team-oriented mindset with strong communication and problem-solving skills.
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  Live in Latin America region.​

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  Experience with
  data orchestration tools
  (e.g., Airflow, Azure Data Factory, or similar).
• 
  Exposure to
  CI/CD for data pipelines
  and deployment automation.
• 
  Experience with
  streaming data
  (e.g., Kafka, Event Hubs).
• 
  Familiarity with data observability and monitoring tools.
• 
  Experience collaborating with
  Machine Learning
  or advanced analytics teams.
• 
  Experience working with Java Spring Boot in data engineering projects.