Sr. Analytics Engineer

<p><strong>Who We Are:</strong></p> <div> <p><strong>Bandwidth</strong>, a prior “Best of EC” award winner, is a global software company that helps enterprises deliver exceptional experiences through voice, messaging, and emergency services. Reaching 65+ countries and over 90 percent of the global economy, we're the only provider offering an owned communications cloud that delivers advanced automation, AI integrations, global reach, and premium human support.&nbsp;Bandwidth is trusted for mission-critical communications by the Global 2000, hyperscalers, and SaaS builders!</p> </div> <p>At Bandwidth, your music matters when you are part of the BAND. &nbsp;We celebrate differences and encourage BANDmates to be their authentic selves. &nbsp;#jointheband</p> <p><strong>What We Are Looking For:</strong></p> <p>We are seeking a <strong>Senior</strong> <strong>Analytics Engineer&nbsp;</strong>to serve as a senior technical authority for the analytics data layer, driving scalable modeling patterns, governance, and performance across multiple domains. In this role, you will architect core datasets and semantic foundations, lead complex cross-functional initiatives, mentor engineers and analysts, and establish engineering and governance patterns for AI agent building and lifecycle management. You will also provide analytics-facing leadership for Sigma Administration and Snowflake Administration consistent with senior expectations.</p> <p><strong>What You'll Do:</strong></p> <ul> <li>Architect the analytics layer: Design enterprise-grade dimensional models, conformed dimensions, and shared marts enabling consistent reporting across domains.</li> <li>Establish standards and governance: Define/enforce modeling conventions, metric definitions, documentation requirements, and data contracts.</li> <li>Lead complex initiatives: Drive cross-team builds/rebuilds and migrations end-to-end with clear impact analysis, sequencing, risk management, and stakeholder alignment.</li> <li>Lead semantic modeling: Define semantic modeling strategy and patterns (entities, relationships, governed metrics) for BI and agent consumers.</li> <li>Lead agent enablement patterns: Establish data structures and governance to support AI agents (grounding/citations-ready provenance, versioned prompts/config where applicable, evaluation datasets, exception queues).</li> <li>Implement observability patterns: Establish monitoring/alerting strategy (freshness/volume/quality) and basic anomaly detection guardrails; mature incident response playbooks.</li> <li>Ensure platform excellence: Create performance and cost guardrails for Snowflake; standardize efficient patterns in dbt and SQL; prevent regressions.</li> <li>Lead Sno