> Markdown version of [/jobs/ext/456200-data-platform-architect](https://www.wearedevelopers.com/jobs/ext/456200-data-platform-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Platform Architect - **Company:** LendingClub - **Location:** San Francisco, CA, United States - **Salary:** $210,000.0 - $245,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Architectural Patterns, Cyber Security, Data Architecture, Information Engineering, Data Infrastructure, Data Integrity, Software Design Patterns, DevOps, Failover, Automation of Marketing, Software Deployment, Systems Integration, AI Infrastructure, Cloud Platform System, Snowflake, IT Architecture, Data Lakes, AI Platforms, Enterprise Integration, Machine Learning Operations, Data Pipelines, Databricks - **Published:** June 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e4f600a715e7295d ## About the Role Do you have experience in Systems integration?, Do you have a Bachelor's degree?, * 12+ years of experience in data engineering, data architecture, or platform engineering; bachelor's degree in a related field or equivalent work experience * You have designed and delivered modern, cloud-based data platforms at scale (Databricks, Snowflake, or equivalent) and understand the trade-offs between performance, cost, governance, and resilience at the architecture level * You operate as a technical authority across teams - your architecture decisions are respected because they're grounded in deep expertise, operational awareness, and a clear accounting of trade-offs * You design for production, not for diagrams - your patterns account for failover, scalability, security, and the operational burden on the teams who build and maintain them * You understand how to apply AI to complex, high-stakes platform architecture - not just for efficiency, but to unlock better outcomes. You set a high bar for responsible use, including attention to data integrity, model limitations, and compliance considerations, and you're building new architectural workflows, not just iterating on existing ones * You're building with AI, not just using it - you have strong instincts about where AI capabilities belong in the platform architecture, how to evaluate build vs. buy for AI infrastructure, and what responsible production deployment looks like in a regulated financial environment * You collaborate naturally across organizational boundaries - working with infrastructure, security, integration, DevOps, and governance teams as a connector, not a gatekeeper * You communicate architecture decisions in business terms, making complex trade-offs understandable to engineering leaders, compliance stakeholders, and executive sponsors Nice to Have * Deep experience with the Databricks ecosystem, including Unity Catalog, Delta Lake optimization, and workspace governance at enterprise scale * Background in enterprise architecture governance at a regulated financial institution (SOX, GLBA, fair lending data requirements) * Experience with ML/AI platform architecture, including MLOps patterns, model serving infrastructure, evaluation frameworks, and feature stores * Familiarity with data mesh or domain-oriented data architecture patterns in large, multi-team organizations ## Description This role defines the technical architecture our data and AI platform - the shared infrastructure that powers origination, marketing, credit decisioning, analytics, and AI capabilities across every business domain. You will set architecture standards, design resilient patterns, and govern platform technology choices that must align across multiple CIO and CPTO teams, while partnering closely with Data & AI Platform Engineering to ensure designs translate into production-grade, cost-efficient systems., * Define and maintain the reference architecture for LendingClub's modern data and AI platform, ensuring it meets enterprise security, compliance, and scalability requirements * Design architectural patterns for data pipelines, storage, compute, and integration that balance performance, cost, and resilience - and validate that what gets built matches what was designed * Govern shared technology choices for the data platform, aligning decisions across infrastructure architecture, security architecture, enterprise integration architecture, and release engineering / DevOps * Develop and enforce standards for platform adoption that other CIO teams must follow, including data modeling patterns, integration contracts, and security guardrails * Lead proofs of concept and technical evaluations for emerging technologies - including AI/ML infrastructure, GenAI integration patterns, and automation platforms - to inform platform roadmap decisions * Drive architecture governance in partnership with the enterprise architecture team, ensuring data platform decisions are consistent with broader technology strategy and regulatory requirements * Identify opportunities to apply AI to improve architecture workflows, from automated impact analysis and capacity planning to intelligent design pattern recommendation and architecture compliance checking ## Related Videos - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [System Resilience: Surviving the Software Storm](https://www.wearedevelopers.com/videos/874-system-resilience-surviving-the-software-storm) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Why Event-Driven Architecture Isn’t About Speed (and When You Actually Need It)](https://www.wearedevelopers.com/magazine/745-why-event-driven-architecture-isn-t-about-speed-and-when-you-actually-need-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)