Engenheiro de Dados (GCP/Databricks) Especialista
New
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- PythonSQLGCPAirflowSparkCI/CDData modelingDatabricksPySpark
Requirements
- Minimum of 5+ years of experience as a Data Engineer in large-scale environments.
- Strong hands-on experience with Google Cloud Platform (BigQuery, Dataflow, Cloud Storage, Pub/Sub).
- Advanced experience with Databricks, Apache Spark, and PySpark.
- Proficiency in Python and SQL for data engineering and processing tasks.
- Solid experience with data modeling (relational, dimensional, and Lakehouse architectures).
- Experience with orchestration tools such as Airflow or Cloud Composer.
- Strong understanding of CI/CD practices, Git version control, and modern engineering workflows.
- Experience working with large-scale, distributed data systems.
- Strong communication skills and experience collaborating with technical and business stakeholders.
- English proficiency (intermediate to advanced) is a plus.
- Previous experience leading technical teams or data projects is highly valued.
Responsibilities
- Lead the design and evolution of modern data architectures, including Data Lake, Data Warehouse, and Lakehouse models.
- Develop, optimize, and maintain scalable ETL/ELT pipelines for batch and streaming data processing.
- Act as a technical reference for data engineering teams, providing guidance on architecture, coding standards, and best practices.
- Design and implement distributed data processing solutions using Databricks, Spark, and PySpark.
- Define and enforce DataOps practices, including testing, CI/CD, versioning, and deployment automation.
- Ensure data governance, security, quality, and reliability across all data pipelines and platforms.
- Optimize performance and cost efficiency within Google Cloud Platform environments.
- Integrate multiple data sources and support real-time and batch data ingestion strategies.
- Collaborate with BI, Analytics, and Data Science teams to ensure data availability and usability.
- Participate in strategic decisions regarding data architecture, tooling, and platform evolution.
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