Solutions Architect, Boston
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Aerospike是面向关键任务用例和工作负载的实时数据库,包括机器学习、生成式和代理式AI。Aerospike每秒可支持数百万次事务,延迟为毫秒级,而总拥有成本仅为其他数据库的一小部分。包括Adobe、Airtel、Barclays、Criteo、DBS Bank、Experian、Grab、HDFC Bank、PayPal、Sony……
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职位描述 · 原文
Aerospike is the real-time database for mission-critical use cases and workloads, including machine learning, generative, and agentic AI. Aerospike powers millions of transactions per second with millisecond latency, at a fraction of the total cost of ownership compared to other databases. Global leaders, including Adobe, Airtel, Barclays, Criteo, DBS Bank, Experian, Grab, HDFC Bank, PayPal, Sony Interactive Entertainment, The Trade Desk, and Wayfair, rely on Aerospike for customer 360, fraud detection, real-time bidding, profile stores, recommendation engines, and other use cases. At Aerospike , we dream big and deliver even bigger. Our mission is to unleash the power of the world’s real-time data with a database built for infinite scale, speed, and sustainability . If you're ready to shape the future of data, join us. Solutions Architect, Bay Area Aerospike is looking for a technically sharp, enterprise-focused Solutions Architect to help drive adoption and shape the technical vision of our blazing-fast, real-time data platform across North America. In this high-impact pre-sales role, you’ll partner closely with account teams and executive stakeholders to lead architecture design, translate complex enterprise requirements into scalable blueprints, and prove clear, compelling value for our prospective customers. Based in North America, you’ll serve as the principal technical advisor and enterprise architect throughout the sales lifecycle — from high-level discovery and architectural design to proof-of-concept (POC) governance and post-sale deployment strategy. Whether it’s designing fault-tolerant, high-throughput database topologies, addressing complex data modeling challenges, or leading strategic technical discussions, your expertise will be key to helping enterprise clients realize the power of Aerospike . This is a pre-sales engineering architecture position. Responsibilities Engineer for advantage: Lead technical discovery and design creative, multi-region architectures that solve demanding data, scale, latency, and resilience requirements. Master the data plane: Bring deep expertise in databases, distributed systems, data platforms, and high-throughput workloads—and make complex technology decisions clear to every audience. Sell the architecture and the outcome: Connect technical choices to business value, competitive differentiation, ROI, and the customer’s broader data strategy. Create conviction through proof: Design and lead rigorous POCs, benchmarks, whiteboarding sessions, demos, and architecture reviews that validate performance, scalability, and business impact. Be the technical deal strategist: Partner tightly with Account Executives to shape deal strategy, multi-thread stakeholders, neutralize architectural risk, and advance complex opportunities. Unblock with ingenuity: Diagnose hard customer problems, develop practical solutions, and turn technical objections into reasons to buy. Turn field insight into leverage: Capture recurring requirements, architectural patterns, and competitive intelligence to influence Product, Engineering, Customer Success, partners, and enablement. Close as the trusted technical authority: Lead executive and engineering conversations across North America and help convert technical credibility into regional revenue. Qualifications Command the room: Bring exceptional executive presence, communication, and presentation skills to earn credibility with C-level leaders, principal architects, and developers. Architect across the data stack: Demonstrate deep expertise in NoSQL, relational databases, distributed systems, MPP data warehouses, and specialized platforms such as graph, time-series, and AI/ML feature stores. Build for real-world scale: Bring hands-on Linux experience and a strong understanding of high-concurrency, high-throughput, mission-critical, and zero-downtime systems. Think in distributed systems: Apply practical knowledge of Spark, Hadoop, HBase, HDFS, Hive, Impala, and
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