Top Scala Jobs in Miami, FL

The Senior Backend Engineer develops core product features in ETL and GraphQL, designs APIs, builds ETL pipelines, and collaborates with frontend teams.

The role involves developing high-performance data processing systems, collaborating across teams, and improving data infrastructure at Airbnb. It requires designing scalable data architectures and advocating for engineering quality in data operations.

The Senior Data Engineer will design, build, and maintain data infrastructure, focusing on data pipelines, ETL processes, and data governance while collaborating with cross-functional teams.

Develop and maintain batch and streaming ingestion systems for global geospatial data at scale. Build, deploy, and monitor ETL/data processing workloads on AWS, collaborate with cross-functional teams, mentor engineers, improve operational quality, reduce technical debt, and be on-call to ensure system reliability.

The role involves designing CLI and backend services, maintaining code quality, collaborating on technical issues, and writing documentation.

Develop and design a highly available storage engine, lead technical solutions, collaborate across teams, and mentor junior engineers.

Design and develop backend services in Scala, build RESTful APIs, optimize performance, collaborate with teams, and troubleshoot issues.

Solutions Architects lead the adoption of Databricks' platform, consult on big data architecture, and develop solutions for customers. This role involves technical leadership, prototyping, and building reference architectures while collaborating with sales and technical teams.

Design, implement, monitor, and optimize scalable data pipelines and models at petabyte scale. Ensure reliability and production-grade deployments, automate repeatable processes, collaborate with product and analytics partners, mentor junior engineers, and drive engineering best practices and code quality.

Design, build, and maintain scalable Scala-based Big Data applications and pipelines using Spark and Kafka. Participate in technical design, implement ETL/data modeling and performance tuning, write tests, troubleshoot production issues, and advise stakeholders on best practices to integrate enterprise data into an analytical warehouse.