Define and maintain enterprise target architectures and roadmaps for advanced analytics capabilities
Establish reference architectures, standards, and reusable patterns for data, analytics, and machine learning solutions that teams can adopt consistently
Work closely with data scientists and model developers to translate modeling approaches into production-ready designs, ensuring smooth paths from experimentation to operational use
Partner with data science and engineering teams to ensure machine learning solutions are designed for production use, including scalability, reliability, monitoring, and maintainability
Provide architectural direction for analytics and reporting capabilities, including how curated data sets, semantic layers, and consumption patterns support consistent business metrics and insights
Contribute to the broader data strategy pillars by shaping approaches to data architecture and infrastructure, data management, data security, data delivery, advanced analytics, and business alignment
Provide leadership for data platforms and pipelines, including guidance on integration patterns, reusable components, automation, and operational resilience
Collaborate with security, privacy, and risk stakeholders to ensure data and analytics solutions incorporate appropriate controls, traceability, and compliance readiness
Lead architecture reviews and decision forums, ensuring decisions are documented and aligned to enterprise direction
Define success measures for data and analytics adoption, including delivery efficiency, reliability, reusability, quality, and value realized
Drive simplification and modernization by reducing duplicated tooling and inconsistent practices across data, analytics, and machine learning solutions
Support pilots and early adopters by converting lessons learned into scalable enterprise guidance and repeatable patterns
Influence stakeholders to improve outcomes such as faster time to insight, faster delivery of trusted analytics, more reliable production ML usage, and improved cost transparency for data and analytics workloads

Requirements

15 years of deep hands-on experience delivering machine learning and advanced analytics solutions, with a strong understanding of how models are developed, evaluated, deployed, and supported in real business environments
7+ years of experience creating solution architectures and strategies across multiple architecture domains (business, application, data, integration, infrastructure and security)
Demonstrated ability to set technical direction and drive adoption across teams through practical guidance, strong engineering judgment, and clear communication
Proven experience working directly with data scientists and model developers, translating modeling needs into scalable, supportable, and secure implementation approaches
Hands-on experience with at least one modern analytics or machine learning platform such as Snowflake, Databricks, SAS, Amazon SageMaker, Dataiku, or equivalent
Cloud experience designing or deploying data and analytics solutions on a major cloud platform, with understanding of scalability, reliability, security, and cost considerations
Strong understanding of modern data concepts including pipelines, data modeling, data quality practices, metadata, and consumption patterns for analytics and ML
Ability to partner effectively across engineering, data science, security, risk, and business stakeholders and drive outcomes through influence and collaboration
Strong written and verbal communication skills with demonstrated ability to create clear technical direction and documentation that teams will use
Bachelor’s and/or master’s degree in computer science or related field (information systems, mathematics, software engineering, etc.)

Benefits

Medical, dental, vision and life insurance
Retirement savings – 401(k) plan with generous company matching contributions (up to 6%), financial advisory services, potential company discretionary contribution, and a broad investment lineup
Tuition reimbursement up to $5,250/year
Business-casual environment that includes the option to wear jeans
Generous paid time off upon hire – including a paid time off program plus ten paid company holidays and three floating holidays each calendar year
Paid volunteer time — 16 hours per calendar year
Leave of absence programs – including paid parental leave, paid short- and long-term disability, and Family and Medical Leave (FMLA)
Business Resource Groups (BRGs) – BRGs facilitate inclusion and collaboration across our business internally and throughout the communities where we live, work and play. BRGs are open to all.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

machine learningadvanced analyticsdata architecturedata managementdata securitydata deliverydata modelingdata quality practicescloud solutionsanalytics solutions

Soft skills

leadershipcommunicationcollaborationinfluenceengineering judgmentguidancedocumentationstakeholder engagementproblem-solvingdecision-making

Certifications

Bachelor's degree in computer scienceMaster's degree in computer sciencerelated field certifications