• Lieu: Tampa, Florida
  • Date de publication: 8th Jul, 2019
  • Référence: 67455

The Data Engineer role will be responsible for implementing ETL/ELT processes to ultimately serve the analytics needs of business users for our clients in retail, healthcare, manufacturing, education and financial services. Data Engineers must be proficient in traditional data warehouse/data mart approaches (Kimball-focused), Modern Data Lake architectures, Dimensional Modeling, and cloud based MPP architectures and distributed computing principles. The ideal candidate has several years of experience deploying monitoring, and managing solutions that collect, process, store, and analyze large volume of data, fast moving data or data that has significant schema variability.


Must Have:


* Experience in implementing on-premises or cloud-based data ETL or ELT solutions


* Strong skills and experience with building data warehouse / data lakes on Microsoft SQL Server or Azure platforms


* Strong interest in learning and mastering data manipulation with languages including Spark, Python, and SQL


* Deep knowledge of database languages, concepts, and environments


* Passion for data, business, and information technology


* Curiosity about data and the ability to translate data-driven insights into decisions and actions


* Self-starter, self-managed, quick learner, problem-solver with a positive, collaborative, and team-based attitude



Day-To-Day:


* Implementing ETL or ELT (extract, transform and load) processes using traditional data warehouse or modern data architectures


* Architecting and explaining big data, data lake, and distributed computing principles and being familiar with key modern data platform architectures including Lambda and Kappa architectures


* Utilizing various Microsoft technologies and platforms including data stores (e.g., Azure Data Lake Store, Azure Blob Storage, Azure SQL Data Warehouse, Cosmos DB), messaging systems (e.g., Azure Event Hubs, Azure Event Grid, Apache Kafka) and data processing engines (e.g., Azure Databricks, Azure Data Factory, Azure Functions, HDInsight, Apache Spark, Polybase).


* 90% of the data work will be in IaaS and Azure SQL