Senior Data Engineer
New
J
JobgetherB2B sales intelligence
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English proficiency for reading technical documentation.
- Required Skills
- AWSPythonSQLGitData modelingDatabricksGenerative AIPySpark
Requirements
- Hold a degree in Computer Science, Data Engineering, Information Systems, or a related field, or have equivalent professional experience.
- Have professional experience as a Data Engineer or in a similar role.
- Demonstrate advanced SQL skills and solid knowledge of data modeling.
- Have hands-on experience with Databricks.
- Have practical experience using AI in data-related workflows, such as LLMs or agents for information collection, extraction, classification, or validation.
- Have daily experience with AI-powered development tools such as Claude Code, Cursor, GitHub Copilot, or similar solutions.
- Know Python and PySpark.
- Be familiar with AWS services such as S3, Lambda, and EKS.
- Have experience with Git and automated testing.
- Be able to work independently, communicate results clearly, and collaborate effectively in agile environments.
- English proficiency for reading technical documentation.
Responsibilities
- Develop, maintain, and optimize large-scale data pipelines in Databricks using SQL and PySpark.
- Apply generative AI, LLMs, and agents to automate data collection, extraction, structuring, and validation.
- Integrate internal and external data sources while ensuring traceability and cost efficiency.
- Build and maintain APIs that make data available to platform products.
- Ensure data quality and integrity through automated tests, validation, and monitoring.
- Apply privacy and data protection practices in compliance with Brazil’s LGPD.
- Optimize processing performance and costs across Databricks and AWS.
- Contribute to architecture decisions and define data engineering standards and best practices.
- Use AI-powered development tools throughout the development lifecycle and help the team adopt them.
- Collaborate with Product, Data Science, Platform, and business teams; share knowledge through reviews and pair programming; and document pipelines, business rules, and architecture.
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