Join Dropbox’s Data Platform team to build and evolve the core infrastructure that powers customer analytics, experimentation, and data-driven product decisions across the company. You’ll work on high-scale systems for data ingestion, storage, processing, and platform reliability, collaborating closely with staff engineers and partner teams that produce and consume data.
This role offers hands-on ownership of critical platform components at massive scale: data lake storage: 60+ PB, streaming: 10+ million events/sec, distributed processing: 200K+ job executions/day. You’ll contribute to a major platform modernization effort, including migrating the data lake to new underlying data formats, re-architecting high-scale ingestion patterns, and building mechanisms that enable AI/ML use cases on top of the data lake.
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 platform capabilities that enable reliable ingestion, storage, and processing of customer and product data at scale.
- Contribute to petabyte-scale data lake modernization, including migration to new underlying storage/table formats.
- Develop platform features to support AI/ML workflows and enable leveraging AI on top of the data lake.
- Partner with engineering teams across Dropbox to integrate with the customer data platform and improve usability and adoption.
- Participate in an on-call rotation and help define operational standards for platform services.
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- 3+ years of software engineering experience building production systems.
- Proficiency in at least one general-purpose programming language (e.g., Python, Go, Java or C#).
- Familiarity with batch and/or streaming data systems concepts (e.g., scheduling, backfills, schema evolution, late data, idempotency).
- Experience debugging and operating production services using logs/metrics and incident response practices.
- Experience with big data tooling such as Spark/SparkSQL, Kafka, Hive, Airflow, or Superset.
- Experience with Databricks or other big data platforms (e.g., Snowflake, Redshift, BigQuery).
- Experience with large-scale data lake storage systems and/or table formats (e.g., lakehouse patterns, schema evolution, partitioning).
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