Staff Data Engineer

Dropbox

Role Description

Dropbox is looking for a Staff Data Engineer to join our Analytics Data Engineering (ADE) team within Data Science & AI Platform. You will be responsible for solving cross-cutting data challenges that span multiple lines of business while driving standardization in how we build, deploy, and govern analytics pipelines across Dropbox.

This is not a maintenance role. We are modernizing our analytics platform, upgrading orchestration infrastructure, building shared and reusable data models with conformed dimensions, establishing a certified metrics framework, and laying the foundation for AI-native data development. You will partner closely with Data Science, Data Infrastructure, Product Engineering, and Business Intelligence teams to make this happen.

You will play a crucial role in establishing analytics engineering standards, designing scalable data models, and driving cross-functional alignment on data governance. You will get substantial exposure to senior leadership, shape the technical direction of analytics infrastructure at Dropbox, and directly influence how data powers product and business decisions.

Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more [here](https://dropbox.tech/culture/sharing-our- engineering-career-framework-with-the-world).

Responsibilities

  • Lead the design and implementation of shared, reusable data models, defining shared fact tables, conformed dimensions, and a semantic/metrics layer that serves as the single source of truth across analytics functions
  • Drive standardization of data engineering practices across ADE and functional analytics teams, including pipeline patterns, CI/CD workflows, naming conventions, and data modeling standards
  • Partner with Data Infrastructure to modernize orchestration, improve pipeline decomposition, and establish secure dev/test environments with production data access
  • Architect and implement a shift-left data governance strategy, working with upstream data producers to establish data contracts, SLOs, and code-enforced quality gates that catch issues before production
  • Collaborate with Data Science leads and Product Management to translate metric definitions into reliable, certified data pipelines that power executive dashboards, WBR reporting, and growth measurement
  • Reduce operational burden by improving pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that make on-call sustainable
  • Evaluate and integrate AI-native tooling into the data development lifecycle, enabling conversational data exploration with guardrails and AI-assisted pipeline development

On-call work may be necessary occasionally to help address bugs, outages, or other operational issues, with the goal of maintaining a stable and high- quality experience for our customers.

Requirements

  • BS degree in Computer Science or related technical field, or equivalent technical experience
  • 12+ years of experience in data engineering or analytics engineering with increasing scope and technical leadership
  • 12+ years of SQL experience, including complex analytical queries, window functions, and performance optimization at scale(Spark SQL)
  • 8+ years of Python development experience, including building and maintaining production data pipelines
  • Deep expertise in dimensional data modeling, schema design, and scalable data architecture, with hands-on experience building shared data models across multiple business domains
  • Strong experience with orchestration tools(Airflow strongly preferred) and dbt, including pipeline design, scheduling strategies, and failure recovery patterns
  • Demonstrated ability to drive cross-team technical alignment, establishing standards, influencing without authority, and working across Data Engineering, Data Science, Data Infrastructure, and Product Engineering boundaries

Preferred Qualifications

  • Experience with Databricks(Unity Catalog, Delta Lake) and modern lakehouse architectures
  • Experience leading orchestration or platform modernization efforts at scale
  • Familiarity with data governance and observability tools such as Atlan, Monte Carlo, Great Expectations, or similar
  • Experience building or contributing to a metrics/semantic layer (dbt MetricFlow, Databricks Metric Views, or equivalent)
  • Track record of establishing data engineering standards and best practices in a federated analytics organization

ole Description

We are seeking a motivated Network Engineer to join Dropbox’s Network Engineering team. You’ll work alongside senior and mid level engineers to design, implement, and optimize enterprise network infrastructure across data centers, offices, wireless networks, and cloud environments.

This role provides hands-on opportunities to build expertise in network automation, performance optimization, cloud networking, and security. You’ll support ongoing projects while helping expand the team’s capabilities in automation, observability, and cloud infrastructure consolidation.

Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more [here](https://dropbox.tech/culture/sharing-our- engineering-career-framework-with-the-world).

Responsibilities

  • Monitor, troubleshoot, and optimize networks using Datadog, Kentik NetFlow, and synthetic testing.
  • Support datacenter and studio office network deployments, upgrades, and retrofits.
  • Assist in day-to-day CorpNet operations including stand-ups and vendor calls.
  • Contribute to Aruba Cloud wireless migrations and ongoing management.
  • Support AWS, GCP, Azure, and OCI networking initiatives including VPC design, routing, and security optimization.
  • Assist with Zscaler ZIA/ZPA/ZDX/ZCC projects including DR testing and Business Continuity deployments.
  • Develop, test, and maintain network automation scripts using Ansible and Python.
  • Contribute to automation of datacenter office deployments to reduce manual effort and streamline provisioning.
  • Work with Terraform and CI/CD pipelines (GitHub Actions, Jenkins, ArgoCD, etc.) to enable repeatable deployments.
  • Maintain and improve NetBox, Oxidized, ACL management, and IPAM systems.
  • Document network architectures, procedures, and operational guides.
  • Contribute to knowledge sharing by writing technical runbooks and training materials.
  • Support network segmentation and traffic inspection projects to enhance security posture.
  • Assist in secrets and certificate management across enterprise platforms.
  • Participate in performance optimization initiatives to improve end-user experience.

On-call work may be necessary occasionally to help address bugs, outages, or other operational issues, with the goal of maintaining a stable and high- quality experience for our customers.

Requirements

  • Hands-on experience with Cisco and Juniper platforms.
  • Familiarity with network automation using Ansible, Python, and Terraform.
  • Understanding of cloud networking concepts (AWS, GCP, Azure, OCI).
  • Experience with enterprise networking technologies: DNS, DHCP, VPNs, and Wi-Fi.
  • Knowledge of monitoring platforms: Datadog, Kentik, NetBox, IPAM.
  • Basic familiarity with CI/CD tools (GitHub Actions, Jenkins, ArgoCD).
  • Strong interest in zero trust security (Zscaler) and performance optimization.

Preferred Qualifications

  • Exposure to Aruba wireless (on-prem and cloud).
  • Experience with Zscaler tools (ZIA, ZPA, ZDX, ZCC).
  • Knowledge of Vault or other secrets management platforms.
  • Familiarity with containers and Kubernetes networking.
  • Strong written skills for technical documentation and runbooks.

AI Fluency

AI fluency is a core part of how we work and grow. It’s not about being an expert—it’s about using these tools thoughtfully and effectively to improve your work and support others.

We look for four key behaviors in candidates:

  • Ownership : You use AI responsibly by protecting data, applying sound judgment, and taking accountability for the quality and accuracy of your work.
  • Experimentation: You explore new AI capabilities and apply them to improve workflows within approved tools and practices.
  • Leverage: You use AI to enhance thinking, improve efficiency, and increase your impact and your team’s.
  • Learning: You stay current on emerging AI tools and trends, continuously build your skills, and share what you learn with others.

Together, these behaviors help build a workforce where technology amplifies human judgment, creativity, and impact.

Dropbox supports responsible use of AI for preparation, but misrepresentation of skills or experience is not permitted. See our AI Principles.
Dropbox is an equal opportunity employer. We are a welcoming place for everyone, and we do our best to make sure all people feel supported and connected at work.