Data Engineer II, FinOps

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. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organisations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.

By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.

Box is the Content Cloud - empowering enterprises to manage the entire content lifecycle, securely and at scale. Our Cloud Operations organisation is expanding, and we’re building a modern Ops Management Platform that powers smarter decisions, greater reliability, and cost efficiency across our cloud footprint.

As a Data Engineer on Cloud Operations, you will design and operate the data foundations that drive FinOps insights and SRE outcomes. You’ll partner with data science and FinOps experts to connect diverse data sources, maintain reliable and timely pipelines, and build user-facing interfaces that inform decisions on cloud cost optimization and operational excellence. 

• →Partner with data science, SRE, and FinOps stakeholders to translate scoped business requirements into scalable data solutions.
• →Build and maintain ETL/ELT pipelines that ingest, clean, transform, and aggregate data from disparate systems using GCP services.
• →Implement and monitor data quality checks, testing, and observability to ensure trustworthy, timely datasets and dashboards.
• →Support incident triage for pipelines; troubleshoot issues, document root causes, and implement fixes with guidance as needed.
• →Develop and maintain user-facing data assets (e.g., curated tables, views, Looker/BI dashboards) for cloud cost and operational reporting.
• →Contribute to engineering best practices through code reviews, documentation, and sprint ceremonies; proactively suggest improvements.
• →Collaborate cross-functionally with Finance, Engineering, and leadership to align data deliverables to business priorities and timelines.
• →Leverage AI tools to accelerate development, testing, and analysis, driving higher-quality, faster outcomes.

We are an AI-first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box.

• At least 2 years of experience working with large-scale data in industry or relevant academic research.
• Strong SQL skills with experience in relational databases; familiarity with dimensional data modeling and query performance tuning.
• Hands-on experience with Google Cloud Platform services such as BigQuery and one of: Dataflow, Dataproc, or Cloud Data Fusion.
• Proficiency with a programming language for data engineering (preferably Python) and common data libraries/frameworks.
• Experience building and operating ETL/ELT pipelines; comfort integrating data via APIs and working with structured and semi-structured formats.
• Practical knowledge of version control (Git), CI/CD workflows, and Infrastructure as Code concepts.
• Effective collaborator with clear written and verbal communication; able to work across Finance, Engineering, and senior stakeholders.
• Nice to have: Exposure to cloud finance (FinOps), cost allocation, and optimization strategies; experience with Looker or similar BI tools.