Data Platform Engineer - Data Services

The Data Platform team builds and operates systems to centralize all of Coinbase's internal and third-party data, making it easy for teams across the company to access, process, and transform that data for analytics, machine learning, and powering end-user experiences. As an engineer on the team you will contribute to the full spectrum of our systems, from managing foundational processing and data storage, to building and maintaining scalable pipelines, to developing frameworks, tools, and internal applications to make that data easily and efficiently available to other teams and systems. We have a number of open roles within the Data Platform team, each with a different focus area; read on to see if you’ll be a good fit for Data Services! What we look for across all focus areas: • 3+ years of relevant work experience; 5+ years for a senior role. • Strong Python, Go, or Java backend development skills. • General experience working with data systems or data pipelines. • Familiar with design patterns such as scale-out, caching, key/value, and columnar. • Leverage SQL, Python, Airflow, and BI expertise to analyze data for operational insights. Nice to haves: • Experience with core AWS/GCP services and components (S3/GCS, IAM, K8s, auto-scaling groups, Terraform), or cloud DevOps experience. What you’ll be doing: • Design, build, and operate our foundational data-heavy services: storage (cloud data warehouse, data lake), orchestration (Airflow), batch processing (Spark, SQL), streaming services (Kafka), query federation and caching, time-series db, graph db, and real-time event aggregation stores. • Build and maintain data integration & process SDKs for use by internal services and product teams throughout Coinbase. • Design and build self-service applications to empower our users to manage and troubleshoot their own data pipelines running on our platforms. • Design and build services for end-to-end data security and data observability: managing access controls across multiple storage and access layers, tracking data quality, cataloging datasets and their lineage, usage auditing. • Convert the functional requests from data analysts, ML, and security & compliance into reusable and scalable patterns. Assemble the data microservices into data platforms for critical business verticals and user cohorts.