What youll do

• Design, build, and maintain reliable and scalable data pipelines to support analytics, reporting, and future ML use cases
• Contribute to our modern cloud-based data platform in Azure and Databricks
• Collaborate closely with Data Analysts and Data Scientists to co-create data products
• Support and contribute to our internal data platform alongside our Data Platform Engineer
• Continuously improve data quality, documentation, and performance
• Champion best practices in DataOps: CI/CD, monitoring, alerting, testing, and infrastructure-as-code

What youll need

• 5+ years of experience in data engineering, with hands-on exposure to analytics engineering practices (e.g., data modeling, transformation logic)
• Deep understanding of data pipeline orchestration, distributed processing, and building resilient, testable ETL/ELT systems 

• Expertise in Scala and experience using Spark in production environments
• Experience with cloud-native architectures
• Familiarity with streaming data frameworks (Kafka, Event Hubs, or similar)
• Solid grasp of data modeling concepts, especially in the context of analytics and reporting (conceptual/logical/physical models)
• Languages & Frameworks: Scala, Python, SQL, Spark
• Cloud & Infra: Azure Cloud Platform, Databricks, Azure DevOps/GitHub, Terraform

What youll bring

• Excellent communication skills with the ability to explain complex technical concepts
• Strong collaboration mindset to work effectively with cross-functional teams
• High attention to detail with a strong focus on data quality, accuracy and reliability
• Strong organizational skills and the ability to drive initiatives from concept to completion