What you'll do
1. Design, build, and maintain reliable and scalable data pipelines to support analytics, reporting, and future ML use cases
2. Contribute to our modern cloud-based data platform in Azure and Databricks
3. Collaborate closely with Data Analysts and Data Scientists to co-create data products
4. Support and contribute to our internal data platform alongside our Data Platform Engineer
5. Continuously improve data quality, documentation, and performance
6. Champion best practices in DataOps: CI/CD, monitoring, alerting, testing, and infrastructure-as-code
What you'll need
7. 5+ years of experience in data engineering, with hands-on exposure to analytics engineering practices (e.g., data modeling, transformation logic)
8. Deep understanding of data pipeline orchestration, distributed processing, and building resilient, testable ETL/ELT systems
9. Expertise in Scala and experience using Spark in production environments
10. Experience with cloud-native architectures
11. Familiarity with streaming data frameworks (Kafka, Event Hubs, or similar)
12. Solid grasp of data modeling concepts, especially in the context of analytics and reporting (conceptual/logical/physical models)
13. Languages & Frameworks: Scala, Python, SQL, Spark
14. Cloud & Infra: Azure Cloud Platform, Databricks, Azure DevOps/GitHub, Terraform
What you'll bring
15. Excellent communication skills with the ability to explain complex technical concepts
16. Strong collaboration mindset to work effectively with cross-functional teams
17. High attention to detail with a strong focus on data quality, accuracy and reliability
18. Strong organizational skills and the ability to drive initiatives from concept to completion