Data Engineer

Summary

Seasoned Data Engineer with a Bachelor of Science in Software Engineering, extensive experience in various facets of data engineering, cloud architecture, and business intelligence. With a strong foundation in Python, PySpark, SQL, DataOps and a plethora of ETL tools, I have been at the forefront of data-driven initiatives across multiple industries and startups.

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Proficient in Data Engineering using Python, Spark, SQL, PDI, Hop, Airflow, Azure Synapse Analytics, DBT, Databricks, and various ETL tools.
Experienced in Cloud architecture with Azure, GCP, and AWS.
Skilled in managing BI tools like Looker and implementing Data Governance with OpenMetadata and other catalog tools.
Expertise in data ingestion and integration utilizing tools such as PDI, Apache Hop, Databricks, AirByte, scraping, and APIs.
Familiarity with Modern Data Stack and Ops architectures including CI/CD, IaaC, containers, Data Lake, Data Warehouse, Lakehouse, Data Mesh, ETL, and ELT concepts.
Portfolio Project: WiseDeal
Fluent in Portuguese, English, German, and Spanish.

Expectations

Challenging Projects, Continuous Learning Opportunities, Impactful Contributions, Opportunities for Tech Leadership.

Employment Preferences

Expected Base Salary

**,000 USD

Expected Hourly Rate

** USD/hr

Academic Degree

Experience

Skills

• Python
• Data
• Lakehouse
• Warehouse
• Lake
• Spark
• SQL
• PDI
• Hop
• Airflow
• Azure Synapse Analytics
• DBT
• Databricks
• ETL Tools
• Azure
• GCP
• AWS
• Looker
• OpenMetadata
• Data Ingestion
• Data Integration
• Apache Hop
• AirByte
• Scraping
• API
• Modern Data Stack
• Ops Architectures
• CI
• CD
• IaaC
• Container
• Data Lake
• Data Warehouse
• Data Mesh
• Snowflake
• GitHub
• GreatExpectations
• PySpark
• BigQuery
• Pentaho PDI
• Data Visualization
• Data Governance
• Observability
• GDPR Awareness
• Version Control
• Software Engineering Best Practices
• Data Engineering
• Data Engineer
• Data Lifecycle
• Lifecycle
• Data Maturity
• Data Quality
• Data Catalog
• DataOps
• DevOps