Senior Analytics Engineer

Bandwidth
, 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. Bandwidth is trusted for mission-critical communications by the Global 2000, hyperscalers, and SaaS builders!

At Bandwidth, your music matters when you are part of the BAND. We celebrate differences and encourage BANDmates to be their authentic selves. #jointheband

We are seeking a Senior
Analytics Engineer 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.

• Architect the analytics layer:
  Design enterprise‑grade dimensional models, conformed dimensions, and shared marts enabling consistent reporting across domains.
• Establish standards and governance:
  Define/enforce modeling conventions, metric definitions, documentation requirements, and data contracts.
• Lead complex initiatives:
  Drive cross‑team builds/rebuilds and migrations end‑to‑end with clear impact analysis, sequencing, risk management, and stakeholder alignment.
• Lead semantic modeling:
  Define semantic modeling strategy and patterns (entities, relationships, governed metrics) for BI and agent consumers.
• 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).
• Implement observability patterns:
  Establish monitoring/alerting strategy (freshness/volume/quality) and basic anomaly detection guardrails; mature incident response playbooks.
• Ensure platform excellence:
  Create performance and cost guardrails for Snowflake; standardize efficient patterns in dbt and SQL; prevent regressions.
• Lead Snowflake Administration (analytics‑facing):
  Own analytics‑oriented administration patterns including RBAC conventions for consumption, access workflows, operational guardrails, and cost governance (production administration expectation).
• Operationalize AI/analytics workflows:
  Define and standardize n8n automation patterns for approvals, escalations, exception queues, and system‑to‑system integrations supporting analytics and agent workflows.
• Represent analytics engineering in architecture reviews and governance councils.
• Participate in tool/vendor evaluations and implementation planning within approved stack and capability categories.

• Bachelor’s degree required;
  Master’s preferred (or equivalent senior‑level experience).
• 5–8+ years relevant experience in analytics engineering, analytics‑focused data engineering, or BI platform engineering.
• Expert SQL and deep experience with dimensional modeling and enterprise metric consistency.
• Significant dbt experience including project architecture, macros, test strategy, templates, and deployments; deep understanding of analytics‑engineering workflows (dbt, governance, testing).
• Demonstrated leadership in driving technical initiatives and mentoring other engineers.
• In‑depth knowledge of generative AI / LLM concepts and enterprise application patterns (agent enablement, governance).
• Working leadership knowledge of production Snowflake administration (analytics‑facing).

• Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent experience.

• Strong Python proficiency for automation, validation frameworks, and data tooling.
• Experience implementing semantic/metric layer tooling…