Engenheiro de Dados Sênior
New
J
JobgetherData Engineering
100% remote work, offering flexibility to work from anywhere in Brazil.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLApache AirflowGitData engineeringCI/CDdbt
Requirements
- 6+ years of professional experience in Data Engineering, including at least 2 years with architectural responsibility.
- Proven experience deploying and operating analytical data platforms in production.
- Advanced SQL skills, including window functions, recursive CTEs, and execution-plan analysis.
- Strong Python experience applied to data engineering, including pandas or Polars and automated testing.
- Hands-on production experience with Apache Airflow, including DAG development and dependency management.
- Experience with a cloud-based MPP data warehouse, preferably Amazon Redshift.
- Practical experience with dbt or an equivalent version-controlled transformation framework.
- Strong knowledge of dimensional modeling/Kimball, including fact and dimension tables and SCD Types 1 and 2.
- Experience with Git, code review, and CI/CD practices.
- Proven ability to diagnose and optimize query performance.
- Strong attention to numerical accuracy and data correctness in auditable environments.
- Demonstrated ability to mentor engineers and act as a technical reference.
Responsibilities
- Design, build, and maintain production-grade data ingestion and transformation pipelines orchestrated with Apache Airflow.
- Develop and evolve dbt transformation models, organizing staging, intermediate, and mart layers with appropriate testing and version control.
- Define and evolve the data warehouse architecture, selecting appropriate modeling approaches for reliability and auditability.
- Establish and enforce MPP warehouse standards, particularly within Amazon Redshift, for distribution, sorting, and optimization.
- Lead database and query performance optimization to balance performance and cost.
- Implement data observability and automated quality controls to ensure data reliability.
- Define data layers and contracts between teams to establish sources of truth and ownership boundaries.
- Ensure end-to-end data lineage and traceability for reporting and regulatory compliance.
- Act as a technical reference for other engineers through mentoring and architectural guidance.
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