Senior Data Engineer

• Develop applications and data pipelines with Python
• Process large volumes of data in a distributed environment using PySpark
• Build and govern data transformations using dbt
• Orchestrate pipelines with Airflow
• Use GitHub for version control, pull requests, code reviews, and CI/CD integration
• Work with AWS services, including Lambda, CloudWatch, S3, Redshift, and Aurora
• Implement and work with Apache Iceberg
• Apply Data Lakehouse and Medallion Architecture patterns
• Execute ETL/ELT processes
• Contribute to building an innovative financial platform from the ground up, with no technical legacy

• Strong experience with Python, developing applications and data pipelines with a focus on performance, maintainability, and best practices
• Experience with PySpark for distributed processing of large volumes of data
• Advanced SQL proficiency
• Experience with dbt for building and governing data transformations
• Knowledge of Airflow for pipeline orchestration
• Strong experience with GitHub, including version control, pull requests, code reviews, and CI/CD pipeline integration
• Knowledge of AWS, especially Lambda, CloudWatch, S3, Redshift, and Aurora
• Experience with Apache Iceberg
• Knowledge of Data Lakehouse architecture and Medallion Architecture
• Experience with ETL/ELT processes
• Nice to have: experience with Databricks
• Nice to have: knowledge of Kafka, particularly in building producers and event-driven messaging systems

• End-to-end professional growth opportunities
• Healthy work environments
• Real-world, high-impact projects
• An environment that fosters innovation, technical ownership, and autonomy
• The opportunity to build a platform from the ground up, with no technical legacy
• Use of best-in-class tools, best practices, and innovative ideas