Data Engineer - Spark Pipelines & Data Modeling

Dropbox; United States; Remote; USD 107,000 - 164,000; Full time

Job summary

Dropbox is seeking a Data Engineer on Analytics Data Engineering to build and operate pipelines and data models used across products and business analytics. You will own well-scoped pipelines end to end—design, build, test, ship, monitor—with senior engineers for architectural calls.

You'll feed datamarts and KPIs used by data science, product, and leadership. Expect a build-focused team on a modern stack, with opportunities to own a full data domain and grow your capabilities.

Qualifications

• 2+ years in Spark, Python, Java, C++, or Scala.
• 2+ years SQL, incl. query performance tuning.
• 2+ years with schema design and dimensional data modeling.
• Experience building/maintaining production data pipelines.
• Exposure to a cloud data lake/lakehouse, Databricks preferred.
• Clear written and verbal communication with non-engineering partners.
• BS in CS or related technical field, or equivalent experience.

Responsibilities

• Build and maintain Spark/SparkSQL jobs populating data models.
• Own end-to-end pipelines from requirements to deployment and monitoring.
• Contribute to data quality frameworks and data lineage tooling.
• Partner with data scientists, analysts, product managers, and engineers.
• Extend datamarts and data models for recurring reporting across products.
• Improve reliability and cost efficiency of pipelines, dashboards, and frameworks.
• Participate in on-call rotation and help improve runbooks/alerts.

Skills

Spark

Python

Java

C++

Scala

SQL

Dimensional modeling

Data modeling

Education

BS in Computer Science or related technical field

Tools

Databricks

Airflow

Job description

Dropbox is seeking a Data Engineer on Analytics Data Engineering to build and operate pipelines and data models used across products and business analytics. You will own well-scoped pipelines end to end—design, build, test, ship, monitor—with senior engineers for architectural calls.

You'll feed datamarts and KPIs used by data science, product, and leadership. Expect a build-focused team on a modern stack, with opportunities to own a full data domain and grow your capabilities.