Field Architect

Illumination Systems Arizona

. Lead technical discovery with customers and partners and design end-to-end architectures.

Posted 10/6/2026full-timeRemote • Saudi ArabiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and integrating end-to-end data platforms, including AI/ML architectures and data lakehouse environments. Proven ability to lead technical discovery, manage customer PoCs, and communicate effectively with technical and executive stakeholders.

Highest-signal resume keywords

End-To-End Data Platform DesignAI/ML Architecture ExpertiseKubernetes and Container ManagementCustomer PoC LeadershipObject Storage and S3-Compatible Architectures

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data IngestionCDCBatch ProcessingStreaming ProcessingLakehouse ArchitectureData QualityGovernanceAI/Analytics WorkloadsLinuxNetworking

Soft Skills

Strong Verbal CommunicationPresentation SkillsCollaborative TeamworkCustomer Success OrientationIntellectual Curiosity

Tools & Technologies

KubernetesKafkaDebeziumNiFiSparkFlinkTrinoStarburstIcebergDelta Lake

Industry Keywords

Cloud EnvironmentsPublic CloudHybrid CloudData LakehouseTechnical Discovery

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformKafkaKubernetesLinuxSpark

About the role

Key responsibilities & impact

Lead technical discovery with customers and partners and design end-to-end architectures.
Design solutions across the full data stack, including ingestion, CDC, batch and streaming pipelines, processing, lakehouse storage and table formats, catalogs, query and analytics engines, orchestration, data quality, governance, and AI integration.
Define and validate architectures for AI/ML training and inference, analytics, lakehouse environments, high-performance data pipelines, and modern application data services.
Provide subject-matter expertise in AIStor, object storage, S3-compatible architectures, distributed systems, replication, erasure coding, security, performance tuning, and large-scale namespace design.
Build trusted relationships with customer technical stakeholders and guide architecture decisions.
Plan and conduct customer PoCs from scope through final technical review, including deployment, integration, testing, troubleshooting, documentation, and production planning.
Help customers move from PoC to production and expand adoption.
Provide technical leadership on Kubernetes, containers, automation, and deployment patterns across private, public, and hybrid clouds.
Integrate data sources, ingestion and streaming tools, processing and analytics engines, AI platforms, identity systems, observability tools, and infrastructure components.
Deliver architecture presentations, whiteboard sessions, demonstrations, workshops, and technical enablement.
Lead or contribute to RFP, RFI, and technical responses.
Capture customer requirements and field feedback and work with Product and Engineering teams to resolve gaps and inform priorities.
Stay current on object storage, data lakehouse, AI infrastructure, analytics, Kubernetes, cloud, and competing data platforms.

Requirements

What you’ll need

10+ years of experience in a senior customer-facing technical role such as Field Architect, Solutions Architect, Sales Engineer, Systems Engineer, or Technical Consultant.
Hands-on experience designing, integrating, and troubleshooting end-to-end data platforms, including several of ingestion, CDC, batch and streaming processing, lakehouse architecture, catalogs, query engines, orchestration, data quality, governance, and AI/analytics workloads.
Demonstrated experience leading customer PoCs, including defining success criteria, deploying and integrating components, validating functionality and performance, resolving technical issues, and presenting results.
Strong understanding of object storage and S3-compatible architectures, including security, replication, data protection, performance, and scale-out design.
Hands-on experience with Linux, networking, storage systems, containers, Kubernetes, and modern data center infrastructure.
Practical experience with relevant data stack technologies such as Kafka, Debezium, NiFi, Spark, Flink, Trino, Starburst, Iceberg, Delta Lake, or data catalogs; experience with every listed technology is not required.
Experience designing or supporting AI, machine learning, analytics, lakehouse, or large-scale data pipeline environments.
Experience with public and hybrid cloud environments, including AWS, Azure, GCP, OCI, or private cloud platforms.
Ability to lead technical discovery, design architectures, build demonstrations, analyze performance, troubleshoot integrations, and communicate tradeoffs clearly.
Strong verbal, written, whiteboarding, and presentation skills for technical and executive audiences.
Ability to work collaboratively across customers, partners, Sales, Product, Engineering, and Customer Engineering teams.
Strong customer-success orientation, intellectual curiosity, sense of ownership, and ability to prioritize effectively in a fast-moving environment.
Willingness to travel as needed to support customers, partners, field events, and internal team activities.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field; equivalent practical experience will also be considered.