Design and develop scalable data engineering pipelines using Azure Databricks and PySpark.
Build ETL and ELT workflows to ingest, transform and prepare large volumes of structured and unstructured data.
Develop reusable Python and Spark components while maintaining clean, efficient and production-ready code.
Work with Azure data services, data lakes and cloud storage to support enterprise analytics requirements.
Optimize Spark jobs, queries and data processing workloads for performance, reliability and cost efficiency.
Translate business requirements into technical specifications and contribute to proof-of-concept development.
Implement data quality checks, monitoring and error-handling mechanisms across production pipelines.
Collaborate with data scientists, analysts and engineering teams to deliver reliable data products.
Design and develop scalable data engineering pipelines using Azure Databricks and PySpark.
Employer opportunity.