Principal GCP Data Engineer Remote USC/GC or H4EAD ONLY ON W2 Required Skills : Needs experience in Airflow or Cloud Composer orchestration, development of new DAGs from scratch. Development of data ingestion and ETL pipelines from scratch. Using SnapLogic primarily for data pipelines and integrations, but also Python, SQL, Dataflow, Spark. Needs experience in data warehousing, Google BigQuery. Understanding of analytics as a whole, how data moves from source, warehouse, semantic or reporting layer, models, and reporting/BI, but their hands-on focus will be around building data pipelines and orchestration. Years of experience: Principal 5-6 years roughly - More about what they've done i.e. healthcare experience, exposure to more than one project, communication. Remote: yes, 9-3 CST are core hours Pain Points: candidates who do not have the correct focus area (building the data pipelines), candidates who have not built something from scratch; either they are maintaining existing data workflows or processes in production, but not building. Overview: Have multiple needs for Associate Data Engineer, Senior Data Engineer, and Principal Data Engineer to support multiple ongoing initiatives including migration of data to GCP, building out the GCP BigQuery warehouse (and moving data from Redshift into it), sunsetting legacy ETL (Datastage) and replacing with various new data ingestion solutions. • Experience in SnapLogic is strongly preferred; they have found this to be more amenable to upskilling. • Needs experience in Airflow or Cloud Composer orchestration, development of new DAGs from scratch. • Development of data ingestion and ETL pipelines from scratch. Using SnapLogic primarily for data pipelines and integrations, but also Python, SQL, Dataflow, Spark. • Needs experience in data warehousing, Google BigQuery. • Not responsible for building out visualizations; another team handles • Will be supporting data modeling, but not owning. Should have some experience in data modeling, data warehousing fundamentals. • Understanding of analytics as a whole, how data moves from source, warehouse, semantic or reporting layer, models, and reporting/BI, but their hands-on focus will be around building data pipelines and orchestration. • Proactive communicators, inquisitive people, problem-solvers, unafraid to make suggestions, ask questions. "Order taker" and "heads down" types of Engineers will not be a culture fit for the team. • They have a list of other technologies in their environment in smaller amounts/more dispersed--any would be a "nice to have": i.e. Kafka, Java, Apache Beam, Alteryx, etc.