Analytics 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 serves 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 amounts of data
• Experience building and optimizing scalable data pipelines, architectures, and data sets
• Expert in SQL
• Experience with at least one programming language: Scala or Java
• Experience with scripting languages: Python or Node.js
• Experience with GCP services including BigQuery, Dataproc, Dataflow/Fusion
• Experience with big data tools such as Hadoop, Spark, Kafka
• Strong analytic skills related to working with structured and unstructured datasets
• Experience supporting and working with cross-functional teams in a dynamic environment
• Familiarity with virtualization/container abstractions and orchestration tools like Kubernetes and Docker
• Familiarity with visualization software such as 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 that meet functional and non-functional business requirements
• Identify, design, and implement internal process improvements such as automating manual processes, optimizing data delivery, and redesigning infrastructure for scalability
• Build infrastructure for optimal extraction, transformation, and loading of data from various sources using GCP BigQuery and Spark
• Build analytics tools that provide actionable insights into operational efficiency and key business metrics
• Work with stakeholders including Executive, Product, Data, and Design teams to assist with data-related technical issues and support data infrastructure needs
• Create data tools for analytics and data scientist teams to assist in building and optimizing the product
• Influence across teams and build best practices across the organization