Solution Architect in Melbourne

Position: Solution Architect - AI-Enabled Data Onboarding / Google Sec Ops

The Solution Architect will provide architecture and technical leadership for the AI-enabled, SIEM standards and specifications- driven data onboarding initiative, initially focused on Google Sec Ops and S3/data lake.

The role will shape the MVP architecture, define integration and automation patterns, and work with Cybersecurity, OM, engineering and delivery teams to establish a scalable self-service onboarding capability.

The emphasis is on solution architecture, data onboarding, integration and automation.

Requirements

• Strong experience in solution architecture, preferably involving SIEM, security analytics, observability or data-ingestion platforms.
• Good understanding of SIEM architecture and data onboarding concepts, including ingestion, parsing, normalization, routing and data quality.
• Experience with Google Sec Ops, or comparable enterprise SIEM platforms with the ability to rapidly develop Google Sec Ops expertise.

Understanding of Google Sec Ops Unified Data Model (UDM) and the principles of mapping source telemetry into a common data model.

• Experience with Cribl Stream, including the design of scalable data collection, routing, filtering, transformation and enrichment patterns, and its integration with SIEM platforms such as Google Sec Ops and Splunk.
• Deep expertise in AWS S3-based data architectures, including Parquet data formats, universal/canonical data schemas, schema mapping and transformation, and the design of scalable data ingestion patterns.
• Experience designing API-based integrations and automation between enterprise platforms.
• Experience designing solutions across cloud and enterprise environments, including identity, connectivity, security and access considerations.
• Ability to translate standards, specifications and governance requirements into technical architecture, controls and reusable implementation patterns.
• Strong stakeholder engagement skills and the ability to work across architecture, cybersecurity, engineering, operations and delivery teams.
• Hands-on experience with Google Sec Ops ingestion mechanisms, parsers, UDM mapping or automation.
• Experience with Splunk enterprise platform
• Understanding of configuration-as-code, CI/CD and automated testing.
• Experience with AI/Gen AI, agentic automation or AI-assisted engineering solutions.
• Familiarity with YARA-L, regular expressions and security detection concepts.
• Understanding of Azure, AWS and/or Google Cloud Platform.

Responsibilities

• Lead the solution architecture and technical design of the AI-enabled self-service data onboarding capability.
• Define the MVP architecture, initially integrating with Google Sec Ops, data lake/S3 while maintaining extensibility to Splunk and Cribl.
• Design the end-to-end onboarding architecture covering self-service intake, AI-assisted assessment, standards- driven validation, workflow, governance, provisioning and verification.
• Translate organisational SIEM standards and technical specifications into reusable architecture patterns, validation controls and automation requirements.
• Define Google Sec Ops, data lake/S3 integration patterns covering ingestion, APIs, collectors, parsers, UDM mapping and configuration automation.
• Define how Cribl will be used within the target onboarding architecture, including reusable patterns for data collection, routing, filtering, transformation and delivery to Google Sec Ops, while supporting coexistence with Splunk where required.
• Establish reusable patterns for log-source onboarding, normalization, validation and data-quality management.
• Define integration patterns with relevant enterprise services such as workflow, identity, secrets management, network services, source control and CI/CD.
• Work with AI and engineering specialists to define how AI-assisted decision-making can safely support onboarding assessment, recommendations and automation.
• Ensure appropriate separation between AI recommendations, deterministic controls, human approvals and automated execution.
• Support the selection and implementation of initial MVP onboarding use cases.
• Identify technical dependencies, constraints, risks and architecture decisions required to progress the MVP into production.
• Provide architecture guidance to engineers implementing the solution and support technical design reviews.
• Work with Cybersecurity, O&M, platform teams and other stakeholders to ensure the solution is secure, scalable, supportable and aligned with enterprise architecture.
• Support production readiness and capability handover following successful implementation.
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