Head of Analytics Engineering, Risk

<p>Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.</p> <p><strong>The Role</strong></p> <p>We are looking for a senior leader to join and lead the Analytics Engineering team within Block's Risk Analytics Data Science team. This group oversees all risk analytics and data science work across Block (including Cash App, Afterpay, and Square).</p> <p>You will be a part of Block's Risk organization and report to Block's Head of Risk Analytics and Data Science, who reports directly to Block's Risk Lead (akin to Chief Risk Officer). You will partner closely with senior product, business, engineering, machine learning, and operations leaders to build a robust and reusable data foundation for Block's Risk Organization. The Risk organization leads with technology and is very data driven; this role will play a large role in strategy, decision making, enhancing risk management capabilities, the deployment of AI initiatives, and supporting the growth of the company.</p> <p><strong>You Will</strong></p> <ul> <li><strong>Lead and scale</strong> the Analytics Engineering team within Block's Risk organization, setting clear vision, culture, and execution standards.</li> <li><strong>Own the roadmap, vision, and delivery</strong> for the data platform, internal data products, and analytics infrastructure that power risk decisioning across Block.</li> <li>Oversee the development of <strong>data models, metrics frameworks, semantic layers, and data governance standards</strong>, ensuring consistency and trust in key business and product metrics.</li> <li>Drive the design and implementation of <strong>end-to-end data pipelines</strong>, from ingestion to transformation to serving layers, in partnership with software engineering and ML/AI teams.</li> <li>Establish and refine <strong>data-product practices</strong>, including user research, product requirement definition, roadmap prioritization, documentation, and feedback loops with internal stakeholders.</li> <li>Ensure the data platform supports <strong>advanced analytics, experimentation, and ML/AI workloads</strong>, enabling teams to build, measure, and iterate quickly.</li> <li>Champion <strong>data reliability, observability, and operational excellence</strong>, including SLAs, incident response processes, and ongoing quality initiatives.</li> <li>Build and manage a high-performing leadership bench across <strong>data engineering, analytics engineering, and data product management</strong>, developing future leaders and supporting career growth at every level.</li> <li>Influence company-wide decision-making by advocating for <strong>data literacy</strong>, promoting best practices, and ensuring the organization makes high-quality, data-driven decisions.</li> </ul> <p><strong>You Have</strong></p> <ul> <li><strong>10+ years of experience</strong> in data engineering, analytics engineering, data product management, or related technical disciplines.</li> <li><strong>5+ years of experience managing technical teams</strong>, including experience developing leaders and managing managers.</li> <li>A demonstrated track record of <strong>building and scaling data platforms, analytical systems, and internal data products</strong> at high-growth tech companies.</li> <li>Deep expertise in <strong>modern data architectures</strong>, including lakehouse patterns, real-time/streaming systems, orchestration, warehouse modeling, ... (truncated, view full listing at source)