What You'll Do
Design and deploy scalable ETL and ELT pipelines using Databricks, Spark, and SQL to enable reliable data flows across enterprise systems. Develop and maintain data lakes and lakehouse environments that support advanced analytics, AI, and business intelligence workloads.
Collaborate with data architects and analysts to integrate data from diverse sources, ensuring consistency, quality, and accessibility. Optimize Spark-based workloads and data models for performance and efficiency, and contribute to the evolution of reusable engineering patterns and best practices.
Support the modernization of legacy data platforms by migrating existing ETL processes to cloud-native solutions. Work within Agile teams to deliver data solutions that align with customer needs and technical standards, engaging directly with stakeholders to understand requirements and deliver impactful outcomes.
Requirements
- 2–5 years of professional experience in data engineering or a closely related field
- Proven experience with Databricks and Apache Spark for large-scale data processing
- Strong proficiency in SQL and Python, particularly PySpark
- Familiarity with cloud data platforms, including services on Azure or AWS
- Hands-on work building and maintaining ETL/ELT pipelines and integration workflows
- Understanding of data modeling principles and architectures for data lakes and warehouses
- Experience using Git and adhering to modern software development practices
- Ability to collaborate effectively in Agile environments and consulting roles
- Bachelor’s degree in Computer Science or equivalent practical experience
- Fluency in written and spoken English
Benefits
- Opportunities to work on international projects with diverse teams
- Access to professional development and technical certifications
- Exposure to cutting-edge data technologies and cloud platforms
- Involvement in transformative data modernization and AI initiatives
- Flexible work arrangements with a global work model
