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