Azure Senior Data Engineer

Our customer provides innovative solutions and insights that enable our clients to manage risk and hire the best talent. Their advanced global technology platform supports fully scalable, configurable screening programs that meet the unique needs of over 33,000 clients worldwide. Headquartered in Atlanta, GA, they have an internationally distributed workforce spanning 19 countries with about 5,500 employees. Our partner perform over 93 million screens annually in over 200 countries and territories.

We are seeking a Senior Data Engineer with proven expertise in Databricks PySpark development, comprehensive data modeling experience (including fact/dimension tables, SCD Type 2 and incremental loads) and event-based architecture skills to join our Data Engineering Team and drive the evolution of our Azure-based Data Analytics Platform.

• Develop reusable, metadata-driven data pipelines using Databricks Lakehouse architecture and PySpark
• Design and implement comprehensive data models
• Build robust ETL/ELT solutions with advanced features: Merge operations, SCD Type 2 implementations, etc.
• Implement incremental data loads with idempotency patterns and overlap joins optimization
• Automate and optimize data platform processes with focus on performance and reliability
• Build integrations with data sources and consumers using event-driven patterns
• Cooperate with infrastructure engineering team to set up cloud resources
• Initiate and implement improvements to data platform architecture

• Databricks expertise: proficient in Databricks Lakehouse architecture and PySpark development
• Data modeling mastery: extensive experience in data model definition including fact tables, dimension tables, measures, grain analysis, SCD Type 2, surrogate keys, late-arriving records handling, overlap joins, and incremental loads with idempotency
• Programming: advanced Python and PySpark skills for ETL/ELT development
• Databricks optimization: deep knowledge of optimize, zOrder, Liquid clustering, ACID transactions and performance tuning
• Event-based architecture: proven experience in designing and implementing event-driven data solutions
• Azure data platform: experience working with Azure-based datasets and data pipelines
• SQL proficiency: strong SQL skills for complex data transformations
• Large-scale data processing: experienced in handling large and complex datasets efficiently
• CI/CD: experience in developing automated deployment pipelines
• Networking fundamentals: understanding of basic networking concepts
• Agile methodology: familiar with Scrum and agile development practices

• Understanding of stream processing challenges and Spark Structured Streaming
• Experience with Infrastructure as Code (Terraform, Bicep)
• Experience with containerized applications (Azure Container Apps, Kubernetes)
• Knowledge of Azure cloud native solutions (Azure Data Factory, Azure Function App, Azure Container Instances)