Quality Analytics Engineer III

Generac•Waukesha, WI

About The Position

The Analytics Engineer III leads complex analytical initiatives, from defining data strategy to deploying predictive and analytical solutions that improve product quality, operational performance, and customer experience. The role partners across quality, engineering, operations, and executive leadership to influence strategic decisions, guide analytical investigations, and scale self-service analytics. It combines deep technical skills with business acumen and mentorship. The position architects scalable reporting frameworks and advanced data models across quality, warranty, manufacturing, and operational analytics; oversees high-impact dashboards and executive-level insights; and develops sophisticated analytical and predictive models to identify trends, warranty drivers, failure patterns, process variation, and emerging product risks. It also designs and validates analytical studies of corrective actions, process improvements, quality initiatives, and operational outcomes. The role partners with data engineering on ETL processes and scalable infrastructure, advances data architecture—including real-time integrations—and leads automation and process-efficiency efforts. It standardizes KPIs, promotes data governance and consistent reporting practices, mentors junior team members, and develops training and tools to enable self-service analytics and data-informed decisions.

Requirements

Bachelor's degree in Data Science, Computer Science, Statistics, Analytics, Engineering, Industrial Engineering, or a related field.
At least 5 years of experience in analytics, including significant experience in data strategy, business intelligence, and advanced analytics.
Demonstrated experience supporting quality, warranty, manufacturing, reliability, operational performance, or continuous-improvement initiatives through data-driven insights.
Expert-level SQL and Python (or R).
Deep experience with data modeling, cloud platforms (for example, Databricks), and large-scale data systems.
Advanced proficiency in BI tools and statistical modeling packages.
Experience developing dashboards, scorecards, and analytical solutions that support quality and operational decision-making.
Experience with power generation development and technology.
Strong stakeholder management skills, including influencing without authority.
Proven experience translating analytics into business strategy and executive narratives.
Ability to communicate complex analytical findings to technical and non-technical stakeholders and drive action through data-driven recommendations.

Nice To Haves

Master's degree in a quantitative field.
Experience with trend analysis, root cause analysis, KPI development, statistical analysis, and process-improvement methodologies.

Responsibilities

Act as a thought partner to leaders, identifying opportunities for analytics to improve product quality, reliability, operational effectiveness, and customer satisfaction.
Lead requirements gathering for complex, cross-functional initiatives.
Architect scalable reporting frameworks and advanced data models across quality, warranty, manufacturing, and operational analytics.
Oversee the delivery of high-impact dashboards and insights for executive-level reporting.
Develop analytical and predictive models to identify quality trends, warranty drivers, failure patterns, process variation, and emerging product risks.
Design and validate complex analytical studies evaluating corrective actions, process improvements, quality initiatives, and operational performance outcomes.
Apply statistical techniques to identify trends, anomalies, and continuous-improvement opportunities.
Partner with data engineering to design robust ETL processes and scalable infrastructure.
Drive improvements in data architecture for quality, warranty, manufacturing, supplier, and operational analytics, including real-time data integrations.
Lead initiatives to automate data processes and enhance operational efficiency.
Standardize quality and operational KPIs and promote data governance best practices across teams.
Drive adoption of consistent metrics, reporting methodologies, and data standards for quality performance management.
Provide technical mentorship to junior team members.
Develop training and tools for business users to champion self-service analytics.
Support quality and operational teams in using analytics for data-informed decision-making.