Data Engineer III

Box (NYSE:BOX) is the leader in Intelligent Content Management. Their platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. Founded in 2005, Box simplifies work for leading global organizations and is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.

• 3+ years of relevant industry or academic experience working with large amounts of data
• Experience building and optimizing scalable data pipelines, architectures, and data sets
• Expertise in SQL
• Experience with at least one of Scala, Java
• Experience with scripting languages: Python, NodeJS
• Experience with GCP services including BigQuery, Dataproc, Dataflow/Fusion
• Experience with big data tools like Hadoop, Spark, Kafka
• Strong analytic skills with structured and unstructured datasets
• Experience supporting cross-functional teams in dynamic environments
• Familiarity with virtualization/container orchestration tools such as Kubernetes, Docker
• Familiarity with visualization software: Tableau
• Familiarity with frontend web framework: React

• Work with a team of high-performing data engineers and analysts to identify business opportunities and design scalable data solutions
• Build and own data pipelines that clean, transform, and aggregate data from disparate sources
• Create and maintain optimal data pipeline architecture
• Assemble large, complex data sets meeting functional and non-functional requirements
• Identify, design, and implement internal process improvements such as automating processes and optimizing data delivery
• Build infrastructure for extraction, transformation, and loading of data using GCP BigQuery and Spark
• Build analytics tools for insights into operational efficiency and business performance
• Collaborate with Executive, Product, Data, and Design teams on data-related technical issues and infrastructure needs
• Create data tools to assist analytics and data scientist teams in product optimization
• Influence cross-team best practices across the organization