Staff Forward Deployed Engineer — Real-Time Customer Data

Overview

As a Staff Forward Deployed Engineer, you embed with strategic enterprise customers to design and productize real-time data solutions that drive business outcomes. You bridge product engineering and customer success, translating business goals into scalable architectures. You’ll define the engagement model, set standards for the team, and tackle hard system-level challenges with the customer and Treasure AI engineers. Your work shapes reusable patterns and reference architectures for enterprise-scale real-time data. This role blends deep technical execution with high-impact collaboration to advance Treasure AI’s platform in the field.

Pay / Benefits

• competitive compensation
• 7% employer match pension
• medical and dental cover
• RSU
• up to 26 weeks parental leave
• Carrot health and family-building benefit

Responsibilities

• Define and execute how to close the gap between raw behavioral data and personalized customer experiences in real time.
• Lead multi-phase engagement with customers: Explore & Discover, Define, Build, and Productize, surfacing issues and dependencies beyond stated requirements.
• Translate ambiguous business objectives into clear system-level problem statements and align stakeholders across Customer, R&D, and platform engineering.
• Design streaming architectures that handle late-arriving events, schema drift, backpressure, and sub-second SLAs.
• Drive technical decisions with explicit tradeoffs and rationales, and own the outcomes.
• Distill solutions into reusable reference architectures, implementation guides, and decision frameworks for broader teams.
• Maintain quality and ease of maintenance as the product evolves by creating durable, scalable solutions.

Key requirements

• 8+ years of software or data engineering experience
• 2+ years in a customer-facing, embedded, or solutions engineering capacity
• Strong hands-on experience with streaming and event-driven architectures (Kafka, Flink, Kinesis, Spark Streaming; DynamoDB and MemoryDB/Redis)
• AWS Cloud fluency for designing scalable end-to-end data pipelines (Kafka/Flink, AWS Lambda, Kinesis)
• Solid data engineering fundamentals (pipeline design, SQL, Python, schema modeling, data quality)
• Experience with CDP, marketing automation, or behavioral data-management platforms
• Proven ability to translate ambiguous problems into working systems, including greenfield work
• Comfort leading technical workshops and deep coding sessions; strong written and verbal communication

• problem-solving initiative
• strong communication (verbal & written)
• collaboration across cross-functional teams
• Kotlin, Node/JavaScript, Java/Scala
• Real-time personalization, push notification pipelines, cart-abandonment systems (plus)
• ML model serving in streaming contexts (feature stores, real-time inference)