Data Engineer III

Box (NYSE:BOX) is the leader in Intelligent Content Management. Our 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 including JLL, Morgan Stanley, and Nationwide. 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 data sets
• Experience building and optimizing scalable data pipelines, architectures, and data sets
• Expert in SQL
• Experience with at least one programming language: Scala, Java
• Experience with scripting languages: Python, Node.js
• Experience with GCP services including BigQuery, Dataproc, and Dataflow/Fusion
• Experience with big data tools including Hadoop, Spark, Kafka
• Strong analytical skills with structured and unstructured datasets
• Experience supporting and working with cross-functional teams in dynamic environments
• Familiarity with virtualization/container abstractions and orchestration tools: 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 business requirements
• Identify, design, and implement internal process improvements including automation and infrastructure redesign for scalability
• Build infrastructure for optimal extraction, transformation, and loading of data using GCP, BigQuery, and Spark
• Build analytics tools that provide actionable insights into operational efficiency and key business metrics
• Work with stakeholders to assist with data-related technical issues and support data infrastructure needs
• Create data tools for analytics and data scientist teams to optimize the product
• Influence across teams and build best practices throughout the organization