Data Engineer- Databricks: SQL/Python/PySpark

• Data Pipeline development/orchestration/maintenance
• Creating robust, fit-for-purpose data modelling solutions to address multiple business cases
• Root cause analysis and pro-active investigation to identify potential issues and optimize ETL pipelines, notify end-users and propose adequate solutions
• Prepare documentation for further reference

• Demonstratable delivery experience using Azure Databricks
• Solid delivery experience using ADF, ADLS Gen2, and the Azure Cloud Ecosystem
• Solid SQL skills - Querying syntax, DDL, and DML statements
• Solid Python skills - able to use the Python programming concepts in Databricks Notebooks for data loading, transformation, and exploration, including familiarity with Pandas
• Strong understanding of core Databricks and Spark concepts such as Delta Live Tables, Delta Sharing, Medallion Architecture, Dataframes, Workflows, Unity Catalogue, Delta Live Tables, and UDF's
• Understands the key principles of Data Warehouse design, including Star Schema, F&D design, Dimension and Fact loading patterns, SCD, and CDC concepts
• Experience with the use of MS DevOps for Scrum management and with Repos and use of CI/CD Pipelines between Azure and DevOps
• Proven track record in a data engineering role where the focus has been on developing data pipelines, data warehouses, data lakehouses, and similar data repositories for BI, Reporting and Analytics use

Nice to have

• Any relevant Azure certifications/exams such as: AZ-900, DP-900, DP-203, or DP-500
• Any relevant Databricks certifications: Lakehouse Data Engineer Associate: Lakehouse Data Engineer Professional