Sr. Analytics Engineer

Who We Are:

What We Are Looking For:

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.

What You'll Do:

• 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.

What You Need:

• 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).

Bonus Points:

Education:

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

Experience:

• Strong Python proficiency for automation, validation frameworks, and data tooling.
• Experience implementing semantic/metric layer tooling (category) at scale.
• Familiarity with data cataloging/lineage tooling (category).
• Experience coordinating orchestration patterns with tools such as Prefect.
• Demonstrated success implementing integration frameworks using tools such as n8n (and similar platforms).
• Technical vision with ability to balance innovation and practical implementation.

Technical Skills:

• Expert SQL & architecture: Set modeling direction across domains; design for scalability, maintainability, and cross-domain consistency; define performance standards and anti-patterns.
• dbt architecture leadership: Own dbt project structure, macro strategy, testing strategy, documentation standards, and deployment patterns; prevent regressions via CI/CD guardrails.
• Enterprise semantic modeling strategy: Define semantic modeling patterns (entities, relationships, governed metrics) used consistently across BI and agent consumers; reduce duplicated logic.
• Snowflake Administration (analytics-facing, production): Lead analytics-oriented admin patterns: RBAC conventions for consumption, access governance workflows, cost/performance guardrails, operational runbooks, and environment promotion hygiene.
• Sigma Administration (lead): Own workspace governance: organization/folder strategy, access governance, content standards, usage-quality controls, and enablement patterns.
• Agent building enablement (org patterns): Establish standardized structures and governance to support AI agents: provenance/citation-ready data, versioned configurations where applicable, evaluation datasets, exception handling/review workflows, and lifecycle maintenance patterns.
• Observability engineering: Define monitoring/alerting standards for freshness/volume/quality; implement basic anomaly detection guardrails and incident response playbooks.
• AWS ecosystem awareness: Understand how AWS constraints (security posture, networking, cost governance) affect data access, operations, and analytics/agent enablement.
• Orchestration standards: Define integration patterns with Prefect (dependency design, SLA management, failure handling) and expectations for pipeline reliability.
• n8n platform patterns: Standardize n8n usage for operational workflows (approvals/escalations/exception queues/integrations) supporting analytics and agent operations.

Soft Skills:

• Strategic thinker with ability to align technical solutions with business objectives.
• Strong problem-solving and analytical skills; able to address complex technical challenges effectively.
• Clear communicator; engages effectively with both technical teams and business stakeholders.
• Effective mentor; promotes technical excellence among engineering teams.
• Self-directed, with ability to manage multiple technical initiatives and priorities.
• Detail-oriented with commitment to quality in architecture, code, documentation, and solution design.
• Passionate about leveraging emerging AI technologies and automation platforms to improve business operations.
• Experience contributing to the development of technical talent and engineering capabilities.

The Whole Person Promise:

At Bandwidth, we're pretty proud of our corporate culture, which is rooted in our "Whole Person Promise." We promise all employees that they can have meaningful work AND a full life, and we provide a work environment geared toward enriching your body, mind, and spirit. How do we do that? Well…

• 100% company-paid Medical, Vision, & Dental coverage for you and your family with low deductibles and low out-of-pocket expenses.
• All new hires receive four weeks of PTO.
• PTO Embargo. When you take time off (of any kind!) you're embargoed from working. Bandmates and managers are not allowed to interrupt your PTO – not even with email.
• Additional PTO can be earned throughout the year through volunteer hours and Bandwidth challenges.
• "Mahalo moments" program grants additional time off for life's most important moments like graduations, buying a first home, getting married, wedding anniversaries (every five years), and the birth of a grandchild.
• 90-Minute Workout Lunches and unlimited meetings with our very own nutritionist.