Principal Analytics Engineer

Crisis Prevention Institute•,

About The Position

The Principal Analytics Engineer is the organization’s most senior individual contributor for the enterprise data platform and the end-to-end technical owner of Snowflake and its supporting ecosystem. The role is accountable for platform architecture, ingestion, data transformation, performance, reliability, security, governance, cost optimization, and operational excellence. It reviews and guides architectural decisions across the data team, establishes engineering standards, and enables trusted analytics and AI capabilities, including secure Model Context Protocol (MCP) configurations and governed access to enterprise data. Working across the data team and with technology, security, AI, and business partners, this role drives successful, scalable Snowflake adoption across the enterprise. It provides technical leadership and mentorship without direct people-management responsibility.

Requirements

Bachelor's degree.
At least 10 years of progressive experience in data engineering, analytics engineering, data platform engineering, cloud data architecture, or a closely related field, including ownership of business-critical production data platforms.
Extensive hands-on experience architecting, administering, securing, optimizing, and operating Snowflake in an enterprise environment.
Demonstrated experience designing and owning production ingestion, transformation, orchestration, observability, and data-serving patterns across a modern cloud data stack.
Demonstrated success as a senior technical authority, reviewing architecture, influencing cross-functional decisions, mentoring experienced engineers, and leading resolution of complex platform issues.
Experience partnering with security, infrastructure, application, analytics, AI, and business teams to deliver secure, governed, highly adopted enterprise data capabilities.
Expert-level Snowflake knowledge spanning architecture, administration, virtual warehouses, workload management, performance tuning, cost governance, data sharing, replication, recovery, security, monitoring, and operational support.
Expert SQL and deep knowledge of data modeling, ingestion, transformation, orchestration, testing, observability, version control, release automation, and production engineering practices.
Deep understanding of enterprise security and governance patterns, including least-privilege access, role design, authentication, network controls, encryption, secrets management, auditing, privacy, lineage, and data lifecycle management.
Strong knowledge of AI-enablement patterns for enterprise data, including secure MCP configuration, governed data access, retrieval and tool-integration patterns, and controls for AI-assisted applications and agents.
Exceptional architecture judgment and ability to evaluate tradeoffs across simplicity, scalability, security, performance, cost, governance, resilience, and maintainability.
Ability to lead through technical influence, conduct rigorous design and code reviews, mentor experienced practitioners, establish standards, and build alignment across teams without relying on formal authority.
Strong communication, consulting, and stakeholder-management skills, including explaining complex platform decisions to technical and executive audiences and connecting engineering choices to business outcomes.
Demonstrated ownership, sound judgment, intellectual curiosity, and ability to operate independently across high-impact priorities while partnering across the data team and enterprise.

Nice To Haves

Deep experience with Snowflake-native capabilities, ingestion technologies such as Fivetran, cloud services, CI/CD, infrastructure automation, and enterprise business intelligence platforms such as Power BI.
Experience designing secure AI-ready data architectures, including retrieval patterns, agent or tool integrations, natural-language analytics, vector or unstructured data capabilities, and MCP configurations.
Experience designing enterprise semantic layers, governed metrics, metadata, lineage, data quality, privacy controls, and self-service analytics capabilities.
Experience simplifying or modernizing complex data platforms and leading enterprise-scale platform adoption or migration initiatives.
Relevant Snowflake, cloud architecture, security, data engineering, or Power BI certifications; beneficial but not required.

Responsibilities

Own the strategy, architecture, administration, reliability, and continuous evolution of the enterprise Snowflake platform and its supporting data ecosystem.
Design and oversee scalable ingestion, transformation, orchestration, and data-serving patterns across Snowflake, Fivetran, Power BI, and complementary technologies.
Set and enforce platform standards for performance, workload management, reliability, observability, testing, deployment, disaster recovery, and cost optimization.
Own the technical implementation of Snowflake security, including roles and access controls, authentication, network policies, data protection, auditing, privacy, and secure data sharing, in partnership with Information Security.
Review major data-platform designs, evaluate tradeoffs, reduce unnecessary complexity, and ensure decisions align with enterprise architecture, governance, and long-term maintainability.
Lead technical enablement of AI use cases on governed enterprise data, including secure MCP server and client configurations, tool access patterns, data interfaces, and controls for AI-assisted workflows and agents.
Establish reusable engineering patterns, reference architectures, coding practices, documentation expectations, and review processes to improve quality and delivery across the data team.
Partner with analytics engineers, BI professionals, AI teams, application teams, infrastructure, security, and business stakeholders to deliver trusted platform capabilities and accelerate enterprise adoption.
Diagnose and resolve complex production, performance, integration, and security issues; lead root-cause analysis and drive durable remediation.
Mentor and influence the data team without direct people-management responsibility, raising engineering capability and guiding critical decisions.
Define and monitor platform health, adoption, service levels, cost, and value metrics; maintain a prioritized technical roadmap supporting enterprise Snowflake adoption.