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Salary: USD 180,000 - 225,000 / annual
Materialize is the live context layer for AI agents and applications, enabling engineering teams to use SQL to transform operational data into real-time, trustworthy views. The company is trusted by Notion, Bilt Rewards, and Crane Worldwide Logistics.
As Materialize's first Forward Deployed Engineer, you will embed with a small number of strategic customers and help them build end-to-end solutions with Materialize at the core. You'll join the Go-To-Market organization, partnering with Account Executives and Field Engineering to take customers from signed contract through live deployment and beyond.
You own technical delivery end-to-end: architecture design, building connectors and SQL-based views, planning migrations from existing systems (including dual writes and controlled cutover), designing real-time architectures for fraud detection, dynamic pricing, operational dashboards, and AI agent context, and supporting customers through implementation and production hardening. You'll write and debug complex SQL and streaming pipelines directly in customer environments alongside their engineering teams. A critical part of the role is building the reusable layer—reference architectures, internal tooling, and implementation patterns—that makes each deployment faster than the last. You'll translate field observations into clear product and engineering feedback, identifying friction points and missing features. You'll act as a trusted technical advisor on architecture, scaling, and best practices for real-time data.
The role is based in the NYC office near Astor Place with a minimum of 3 days per week in person. Travel is 30–50% during active customer engagements and less between them. You'll be onsite for kickoffs, critical launches, and production cutovers.
REQUIREMENTS:
- 5+ years of engineering experience, including 1+ years in a post-sales, customer-facing role (forward deployed engineering, solutions engineering, solutions architecture, or technical consulting) with a track record of owning complex enterprise implementations
- Strong SQL and solid understanding of modern data architectures: relational databases, event streaming, and change data capture
- Hands-on experience with streaming or event-driven systems (Kafka, Debezium/CDC, Kinesis, Flink, Spark Streaming, or similar)
- Proficiency in at least one of Python, Java, or TypeScript for building integrations and tooling
- Familiarity with cloud infrastructure (AWS, GCP, or Azure) and containerized environments
- Proven track record taking systems from prototype to production in someone else's environment, including performance tuning, failure modes, monitoring, and handoff
- Comfortable owning ambiguous, high-stakes technical problems with incomplete information and moving without a large support structure
- Excellent written and verbal communication; ability to translate deeply technical trade-offs for both engineers and executives
- Plus: Experience with another streaming SQL engine or modern data warehouse (Snowflake, BigQuery, Redshift)
- Plus: Production experience with AI agents or LLM applications, especially the context layer
- Plus: Experience at a fast-growing startup or as an early member of an FDE or professional services function
