Data Architecture Lead

About the Role

Point Wild is building Lat 61, a next-generation platform designed to rapidly ingest, normalize, and enrich diverse data sources, powering AI agents, model inference stack, analytics, and decision-making systems. To accelerate this effort, we are seeking a Lead Data Architect who will own the technical vision, high-level design, and functional roadmap for the platform's data and foundational layers.

This role blends deep technical leadership with strategic product ownership. You will define the data architecture, ensure it scales across multi-cloud environments, and translate business priorities into a clear technical roadmap. You’ll lead design sessions, make trade-offs under tight timelines, and partner closely with engineering, AI/ML teams, and business stakeholders.

Day to Day

• Own the Data Architecture: Define and evolve the end-to-end technical architecture of the Lat 61 platform, ensuring scalability, interoperability, and performance.
• Platform Ownership: Act as the functional owner for the data and foundational platform, translating strategic goals into platform capabilities, MVP scope, and technical priorities.
• Data Platform Design: Build and maintain multi-modal data strategies, incorporating lakehouse patterns, open data formats , and APIs for data access. This includes architecting our core data pipeline and ingestion services and the data storage components for our tools catalog, large-scale analytical data stores and multi-cloud storage solutions.
• AI/ML Data Enablement: Partner with AI/ML teams to design the scalable infrastructure they require to support advanced AI/ML workflows, including vector databases, graph stores, and retrieval-augmented generation (RAG) frameworks.
• Architect Foundational Services: Lead the design of the platform's foundational data services layer that powers critical components of the AI platform such as workflow orchestration, configuration management, cost metering, telemetry, and authentication/authorization components.
• Multi-Cloud Leadership: Guide deployment across AWS, Azure, and GCP, ensuring portability, resilience, and compliance.
• Stakeholder Alignment: Partner with business leaders to balance innovation with near-term deliverables; lead design sessions and communicate trade-offs clearly.
• Governance & Standards: Establish best practices for data quality, security, lineage, and lifecycle management.

What you bring to the table

• 8+ years of experience as a data architect, including a minimum of 3+ years designing, building, and scaling data platforms to support AI/ML, and product engineering teams in complex, product-driven organizations (e.g., enterprise, B2B SaaS, large-scale technology, or high-growth startups).
• Deep, hands-on expertise in architecting data solutions for AI systems, including designing the data infrastructure for retrieval-augmented generation (RAG) frameworks, vector databases, and graph stores.
• Strong experience with multi-cloud deployments, hybrid data environments, and interoperability.
• Expertise with common data pipeline tools such as Dagster, Apache Spark, Airflow
• Direct experience designing the data architecture to support high-performance model serving (e.g., low-latency caching) and complex, stateful AI agent automations (e.g., data-driven workflow orchestration).
• Strong business acumen, with the ability to align technical vision with organizational strategy.
• Experience serving as a technical lead, balancing competing priorities to deliver high-impact MVPs.

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