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Updated 2026-06-19 05:00 UTC·© 2025–2026 RoleSuite
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Engenheiro de Dados SR

Jobgether · Brazil

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados SR based in Brazil.

You will join a dynamic Data & AI environment focused on designing, building, and scaling modern data platforms that power critical business decisions. The role involves working with large-scale distributed systems and cloud-native architectures, ensuring data is reliable, accessible, and optimized for performance. You will contribute directly to the evolution of robust data pipelines supporting analytics and machine learning initiatives. Operating in a collaborative and fast-paced setting, you will engage with engineering, analytics, and business teams to deliver high-impact solutions. The position offers exposure to modern data stack tools and practices in a highly innovative and growth-oriented ecosystem. You will also play a key role in improving data governance, observability, and architectural best practices.

Accountabilities:

  • Develop, maintain, and optimize scalable end-to-end data pipelines supporting business and analytics needs.
  • Build distributed data processing solutions using Apache Spark for high-volume workloads.
  • Orchestrate workflows, scheduling, and automation using Apache Airflow.
  • Design and implement data engineering solutions in Python following best engineering practices.
  • Work within cloud environments, primarily on Microsoft Azure, ensuring scalable and secure data solutions.
  • Implement CI/CD pipelines to support reliable and automated deployment processes.
  • Use YAML/YML configurations to enable automation, infrastructure definitions, and pipeline standardization.
  • Containerize applications and services using Docker to improve portability and scalability.
  • Build and maintain transformation models using dbt.
  • Collaborate with cross-functional teams to ensure data quality, reliability, performance, and alignment with business goals.
  • Support architectural decisions related to scalability, observability, governance, and data platform evolution.
  • Requirements:

    • Advanced English is mandatory, with strong communication skills for meetings, documentation, and stakeholder interaction.
    • Solid experience working with cloud data environments, especially Microsoft Azure and related services.
    • Strong programming skills in Python.
    • Proven experience with distributed data processing using Apache Spark.
    • Experience building and managing workflows using Apache Airflow.
    • Hands-on experience with CI/CD pipelines and modern DevOps practices.
    • Knowledge of YAML/YML for configuration and automation workflows.
    • Experience using Docker for containerized environments.
    • Experience with dbt for data modeling and transformations.
    • Familiarity with version control, testing, documentation, and software engineering best practices.
    • Differentials:

      • Experience in consulting environments.
      • Knowledge of lakehouse, data lake, or data warehouse architectures.
      • Experience with Databricks, Azure Data Factory, Synapse, or Fabric.
      • Exposure to data observability, monitoring, and data quality practices.
      • Experience with high-scale, production-grade data pipelines.
      • Benefits:

        • Competitive compensation aligned with market standards.
        • Flexible work arrangements (remote or hybrid, depending on project).
        • Health and wellness benefits package.
        • Career development and continuous learning opportunities.
        • Exposure to international projects and cutting-edge data technologies.
        • Collaborative and innovation-driven work environment.
        • Access to training programs, mentorship, and technical communities.

Data & ML pay context

Based on 1,401 disclosed Data & ML salaries on RoleSuite, the role pays a median of $166K/year, with most offers between $128K and $209K (10th–90th percentile: $106K–$250K).

See the full Data & ML salary breakdown →
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