Data Engineer

Role Description
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.
Responsibilities
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
Preferred Qualifications
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)
Durable Skills
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.