Data Engineer

Required skills

5+ years of Spark, Python, Java, C++, or Scala development experience, 5+ years of SQL experience, 5+ years of experience with schema design, dimensional data modeling, and medallion architectures, Experience with the Databricks platform and data lake architectures for large-scale data processing and analytics, Excellent product strategic thinking and communications to influence product and cross-functional teams by identifying the data opportunities to drive impact, BS degree in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience, Experience designing, building and maintaining data processing systems

Preferred skills

7+ years of SQL experience, 7+ years of experience with schema design, dimensional data modeling, and medallion architectures, Experience with Airflow or other similar orchestration frameworks, Experience building data quality monitoring using MonteCarlo or similar tools

Technologies

Spark, SparkSQL, Databricks, data lake architectures, Airflow, MonteCarlo

Responsibilities

Define company data assets (data model), Spark, SparkSQL jobs to populate data models, Define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage, Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable, Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems, Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way, Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains, Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts, 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

Seniority

Domain

Data Science & AI Platform