Senior Staff Software Engineer, Data Platform

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.

What you'll do:

• 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.

Required Skills and Experience:

• 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.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready out…