Senior Analytics Engineer

Crisis Prevention Institute•Milwaukee, WI

1d•Hybrid

About The Position

The Senior Analytics Engineer is a business-facing solution owner responsible for translating complex business needs into trusted, scalable data and analytics solutions. Working as a forward-deployed partner within the business, this role owns solutions from discovery and design through delivery, adoption, and continuous improvement. The position enables reporting, business intelligence, analytics, and AI use cases; curates reusable semantic models; integrates governed data into AI-enabled solutions; and advances practical data governance. The role works across a modern cloud data stack—including Snowflake, Power BI, and Fivetran—while partnering closely with business and technology stakeholders to deliver measurable outcomes.

Requirements

Bachelor’s degree
At least six years of progressive experience in analytics engineering, business intelligence, data engineering, data analytics, or a closely related field, including demonstrated ownership of production data and analytics solutions.
Experience partnering directly with business stakeholders to discover needs, design solutions, deliver value, and support adoption
Hands-on experience delivering solutions on a modern cloud data platform and business intelligence stack.
Advanced SQL and strong knowledge of data modeling, transformation, testing, performance optimization, and analytics engineering practices.
Strong Power BI capabilities, including semantic modeling, DAX, Power Query, report development, deployment, governance, and performance tuning.
Working knowledge of cloud data warehouses, ingestion and transformation patterns, version control, release practices, and production support.
Ability to translate ambiguous business questions into well-scoped, durable technical solutions and to balance speed, usability, scalability, governance, and maintainability.
Strong facilitation, consulting, communication, and stakeholder-management skills, with the ability to influence decisions across technical and nontechnical audiences.
Demonstrated accountability, sound judgment, curiosity, and a product-oriented mindset focused on business value and user adoption.
Ability to work independently across multiple priorities while collaborating effectively with business partners, data professionals, application teams, security, and AI teams.

Nice To Haves

Experience with Snowflake, Power BI, and Fivetran, or comparable tools within a modern cloud data ecosystem.
Experience designing and building enterprise semantic layers, including reusable semantic models, governed metrics, shared business definitions, and self-service analytics capabilities.
Experience enabling AI, machine learning, natural-language analytics, or automation use cases with trusted enterprise data.
Experience with dimensional modeling, data quality, metadata, lineage, access controls, and other applied data governance practices.
Relevant cloud, data platform, or Power BI certifications are beneficial but not required.

Responsibilities

Serve as the end-to-end owner for assigned data and analytics solutions, from business discovery and requirements through design, delivery, adoption, support, and continuous improvement.
Embed with business teams as a consultative, forward-deployed engineering partner to understand workflows, frame problems, recommend solutions, and deliver measurable business outcomes.
Design, build, test, deploy, and maintain reporting, dashboarding, business intelligence, and analytics solutions, primarily using Power BI and complementary AI-enabled tools.
Curate analytics-ready datasets and design, build, and maintain governed semantic layers—including reusable semantic models, shared measures, business definitions, and metadata—that provide consistent, trusted data for self-service analytics, reporting, and AI-enabled solutions.
Develop and optimize data transformations and integrations across Snowflake, Fivetran, Power BI, and other components of the modern cloud data stack.
Partner with AI project teams to prepare, integrate, and operationalize trusted enterprise data for AI-assisted workflows, search, automation, and analytical use cases.
Apply data governance standards in daily delivery, including ownership, definitions, lineage, access, privacy, quality, documentation, and lifecycle management.
Establish validation, monitoring, and support practices that ensure solutions are accurate, reliable, secure, performant, and usable.
Communicate technical concepts clearly to nontechnical audiences; facilitate tradeoff decisions; and provide training, documentation, and change support that drive adoption.
Contribute reusable patterns, peer reviews, mentoring, and engineering standards while maintaining primary accountability for the success of assigned business solutions.