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