Field Architect

Role overview Advise customers on technical architectures for data platforms supporting AI, analytics, cloud-native applications, and large-scale workloads. Lead discovery, design, proof-of-concept work, and performance validation, then help teams move successful evaluations into production. The role combines hands-on engineering depth with customer guidance and collaboration across product, engineering, sales, partners, and customer success teams.

Responsibilities - Lead technical discovery and turn business goals, data sources, application needs, and infrastructure constraints into end-to-end architecture recommendations. - Design and assess data platforms spanning ingestion and change data capture, streaming and batch processing, lakehouse storage, catalogs, query engines, orchestration, governance, and AI integration. - Advise on object storage and S3-compatible architectures, including security, replication, erasure coding, performance, and scale. - Own customer proof-of-concept work: agree on success criteria, deploy and integrate components, test function and performance, troubleshoot issues, document results, and recommend next steps. - Guide cloud-native and Kubernetes deployments across private, public, and hybrid environments; deliver technical presentations, workshops, demonstrations, and written responses. - Share customer and field feedback with product and engineering teams, and support adoption from evaluation through production and expansion.

Requirements - At least ten years in a senior customer-facing technical role, such as solutions architecture, field architecture, systems engineering, sales engineering, or technical consulting. - Hands-on experience designing and troubleshooting end-to-end data platforms, including several areas such as ingestion, streaming, lakehouse systems, query engines, governance, or AI workloads. - Experience leading customer proof-of-concepts from success criteria through technical evaluation and presentation of findings. - Strong knowledge of object storage and S3-compatible systems, plus Linux, networking, storage, containers, Kubernetes, and data center infrastructure. - Practical familiarity with data tools such as Kafka, Debezium, Spark, Flink, Trino, Iceberg, or Delta Lake; experience with every example is not required. - Clear communication and presentation skills, customer focus, sound prioritization, and willingness to travel as needed. - Bachelor’s degree in a related technical field or equivalent practical experience.

Nice to have - Experience with public or hybrid cloud environments and AI, machine learning, analytics, or large-scale data pipeline deployments.