As a Data Engineer on Analytics Data Engineering, you will build and operate the pipelines and data models the rest of Dropbox relies on to understand its products and its business. You will own well-scoped pipelines end to end — design, build, test, ship, monitor — with senior engineers alongside you for the harder architectural calls. Your work feeds the datamarts and KPIs used by data science, product, and company leadership, so the quality of what you build is visible quickly. This is a build-oriented team on a modern stack rather than a maintenance role, and a strong place to develop into an engineer who can own a full data domain.
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.
- Build and maintain Spark and SparkSQL jobs that populate company data models
- Own well-scoped pipelines end to end, from requirements through deployment, monitoring, and iteration
- Contribute to data quality frameworks, testing, and data lineage instrumentation
- Partner with data scientists, analysts, product managers, and engineers to turn data needs into durable models
- Extend datamarts and data models supporting recurring reporting and analysis across products
- Improve the reliability and cost efficiency of existing pipelines, dashboards, and frameworks
- Participate in a business-hours on-call rotation and help improve runbooks and alerting
Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.
Requirements- 2+ years of development experience in Spark, Python, Java, C++, or Scala
- 2+ years of SQL experience, including query performance tuning
- 2+ years of experience with schema design and dimensional data modeling
- Experience building and maintaining production data pipelines that others depend on
- Working exposure to a cloud data lake or lakehouse platform, Databricks preferred
- Clear written and verbal communication with non-engineering partners, and a track record of asking for help and feedback early
- BS in Computer Science or a related technical field involving coding (e.g. physics or mathematics), or equivalent technical experience
- 4+ years of SQL experience
- Experience with medallion architectures and incremental data modeling patterns
- Experience with Airflow or a similar orchestration framework
- Exposure to data quality monitoring using Monte Carlo or similar tools
- Exposure to streaming architectures (Kafka, Kinesis, Structured Streaming)
AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve:
- Awareness: Understand yourself and others.
- Judgment: Evaluate information and make decisions in complex situations.
- Adaptability: Learn, adjust, and stay effective through change.
- Connection: Communicate, collaborate, and build trust.
To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser.
CompensationUS Zone 1
This role is not available in Zone 1
Skills Required
- 2+ years of development experience in Spark, Python, Java, C++, or Scala
- 2+ years of SQL experience, including query performance tuning
- 2+ years of experience with schema design and dimensional data modeling
- Experience building and maintaining production data pipelines
- Working exposure to a cloud data lake or lakehouse platform
- Clear written and verbal communication with non-engineering partners
- Bachelor's degree in Computer Science or a related coding-intensive technical field, or equivalent technical experience
- 4+ years of SQL experience
- Experience with medallion architectures and incremental data modeling patterns
- Experience with Airflow or a similar orchestration framework
- Exposure to data quality monitoring using Monte Carlo or similar tools
- Exposure to streaming architectures such as Kafka, Kinesis, or Structured Streaming
What the Team is Saying
Dropbox Compensation & Benefits Highlights
How does Dropbox ensure its pay and bonus plans are competitive?
Dropbox ensures base pay and bonuses are competitive by benchmarking pay using formal compensation surveys to create salary ranges and bonus targets, and conducting twice-yearly reviews aligned with performance.
Employees describe pay and bonuses as competitive and performance-driven.
Dropbox Insights
What We Do
We're a global community of bold visionaries and resourceful doers who are shaping the future of Dropbox—and with it the future of work. Our Virtual First model combines the flexibility of a distributed workplace with the power of human connection, making space for both meaningful work and meaningful relationships. With our start-up mindset and enterprise-level opportunities, you can be who you are and grow into who you’re meant to be. Here, you can own your impact to make work more intuitive, joyful, and human—for you as a Dropboxer and for hundreds of millions of people worldwide. If you're ready to push boundaries—and yourself—Dropbox is ready for you.
Why Work With Us
We believe people do their best work when empowered with autonomy and harmony, and we understand there’s no substitute for human connection. Our Virtual First model combines the flexibility of remote work with the power of in-person collaboration to create the best of both worlds: a distributed workplace, anchored in community.
Gallery
.jpg)
.jpg)
.jpg)
.jpeg)
.jpg)
Dropbox Offices
Remote Workspace
Employees work remotely.
While remote work is the primary experience for our employees, we also prioritize opportunities for quarterly in-person collaboration knowing that connection is vital to a thriving workforce. We focus on how we work, not where we work.
