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Team: IT
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.
- 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.
- 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.
- 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.
Requirements:
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