As a Senior Staff Software Engineer on the Data Platform team within Platform , you'll define and lead the technical strategy for Coinbase's data infrastructure, spanning ingestion, transformation, warehousing, streaming, and serving systems. This is a foundational role at the intersection of distributed systems, data engineering, and AI-readiness, reporting to the Senior Director of Engineering.
You'll set architectural direction, drive multi-quarter roadmaps, and transition the organization from managed-service dependency toward engineering-built, platform-grade infrastructure that powers everything from fraud detection to modern multi-agent AI architectures.
- Own the technical strategy and architecture for Data Platform, setting direction across data ingestion, transformation, warehousing, streaming, and serving systems while driving engineering-led cost reduction at the infrastructure layer.
- Architect data infrastructure to natively support AI and ML workloads, ensuring pipelines, data lake systems, and compute can power ML training, feature stores, real-time inference, and multi-agent AI architectures at scale.
- Drive the evolution to near-real-time data availability, enabling downstream teams across Coinbase to act on fresher data for fraud detection, financial reporting, and analytics.
- Build alignment and secure commitment from senior leadership and cross-functional partners across product, infrastructure, analytics, ML, privacy, security, and compliance on technical priorities and tradeoffs.
- Serve as the primary technical voice for Data Platform in org-wide forums including OKR planning, architecture reviews, and long-term infrastructure strategy, while elevating senior engineering talent.
- 12+ years building and operating large-scale distributed data infrastructure, including streaming platforms, data lakes, batch and real-time processing engines, and query/serving layers.
- Record of owning end-to-end technical strategy and architecture for an entire data domain across multiple teams, with demonstrated success transitioning organizations from managed-service dependency toward platform-grade, engineering-built infrastructure.
- Deep systems thinking across the full data lifecycle, with ability to reason about performance, reliability, cost, security, and privacy at each layer.
- Demonstrated experience designing data systems that support ML training pipelines, feature stores, real-time inference, or AI agent workloads at scale, with familiarity across technologies such as CDC, Kafka, Databricks, Snowflake.
- Platform mindset and product-manager orientation toward data infrastructure, focused on reducing friction for developer customers and creating compounding leverage.