Key Responsibilities • Contribute to the design and development of scalable data pipelines and a growing data lake • Build and extend data processing workflows using Python, Apache Spark, and Databricks • Define technical standards, best practices, and reusable frameworks for data engineering • Ensure data quality, reliability, performance, and maintainability across data solutions • Support data modeling, data integration, and transformation processes for analytics and reporting • Drive automation, monitoring, and CI/CD improvements to ensure operational excellence • Collaborate across teams, acting as a technical interface between the data platform and engineering, analytics, and business stakeholders. • Contribute to architecture decisions and long-term data platform strategy Your Profile • Outstanding programming experience, preferably in Python; ability to write clean, testable, production-grade code; able to write clean, testable, production-grade code • Strong SQL skills and familiarity with structured and semi-structured data formats (JSON, Protobuf, Delta format) • Hands-on experience with Apache Spark, ideally on Databricks, and understanding of the medallion architecture • Solid grasp of data lakehouse principles, data modeling, and data governance concepts • Experience building and maintaining CI/CD pipelines (e.g. GitLab CI); familiarity with IaC and deployment • Cloud Platforms: Experience with AWS or comparable cloud providers; familiarity with Databricks as a managed Lakehouse platform • Experience with event-driven architectures or streaming platforms (e.g. Kafka) • Proven track record deploying, monitoring, and maintaining data pipelines and services in production environments; experience with testing practices • Able to work autonomously and take ownership of tasks end-to-end • Clear and concise communicator — comfortable working across engineering and data teams Education & experieance • Bachelor's degree in Software Engineering, Mathematics, Physics, or a related field • 3 years of project/coding experience in a company • Cloud experience: • Databricks, AWS/Azure • Staging, testing, Git, pipelines • Distributed systems, data pipelines • Python experience: • Versioning, package management, requirements/environments • Change data capture • Data engineering experience: • SQL • Partitioning • Data structures (tables, relations) • Lakehouse