Principal Engineer, Data Platform
AlphaSense · Remote - United States
About this role
## Principal Engineer, Data Platform ### About AlphaSense AlphaSense is the AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster, more confident decisions in business and finance. The platform combines domain-specific AI with a vast content universe of 500M+ premium business documents (e.g., equity research, earnings calls, expert interviews, filings, news, and proprietary content). It’s built for speed, accuracy, and enterprise-grade security. ### About the Role AlphaSense runs a large-scale, Kubernetes-native platform on a modern microservices architecture, serving the majority of the Fortune 500. The team is moving to a **cell-based architecture**, where each cell is an independent deployment of the platform. The hardest part is **data**. This role focuses on the **Data Platform**—the capabilities that let engineering teams build, run, and scale storage at large scale without needing to be experts in every underlying system. You’ll help set the technical direction for the Data Platform and build it (not just specify it). A central focus is building capabilities for **large-scale data migrations** so the company can create, move, split, and rebalance data across cells and geographies as the architecture rolls out. AlphaSense prefers **managed-first** solutions and expects engineers to **own their storage**: build it, run it, and scale it. Your job is to provide teams the platform, standards, and tooling to do so safely, while owning reliability and scalability for the platform itself. ### Responsibilities - Help set the architecture and roadmap for the Data Platform and the storage ecosystem behind it - Build capabilities for large-scale data migrations across cells, regions, and geographies (move, split, validate, and roll back with minimal downtime) - Enable product teams self-service ways to provision, operate, and scale their own storage - Drive data-layer changes required by the cell-based architecture (data placement, tenant routing, replication, isolation) - Define and apply a managed-first approach (use managed services where they fit; self-manage only with a clear reason) - Identify and close scalability gaps in data stores across regions, cells, and deployment models - Build shared libraries that give product teams self-service storage capabilities - Partner closely with Cloud Platform, SRE, and DevEx teams - Raise the architectural bar and mentor Staff and Senior engineers - Apply AI tooling to data platform work (e.g., slow query reviews and incident diagnosis) ### Requirements - Deep hands-on experience designing and operating data platforms and storage systems - Production large-scale experience with storages (e.g., MongoDB, ClickHouse, Redis, PostgreSQL) - Cloud-native data storage experience, including managed data services on AWS - Strong background in Kubernetes and containers - Track record designing and running large-scale data migrations, including live migrations - Experience with cell-based, multi-tenant, or multi-region architectures - Experience building platforms/tooling used by other engineers; comfort with engineers owning their storage and running what they build - Demonstrated ability to land significant changes in code owned by other teams - Proven ability to spot data scalability and reliability limits before they become incidents - Ability and desire to design, lead, influence, and deliver on ambitious technical road
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