Manager, Data & AI systems architecture

Architecture Leadership: Lead the end-to-end architecture for AI and data systems, ensuring cost-effective scalability, performance, and security across cloud and on-premises environments.
The goal is to build and support a modern data stack.
AI & Data Systems: Design, implement, and manage data infrastructure and AI platforms, including but not limited to AWS, Azure, Google Cloud Platform, Databricks, and other key data tools.
Lead the data model approach for all data products and solutions.
Cloud Expertise: Champion cloud adoption strategies, optimizing data pipelines, analytics workloads, and AI/ML model deployment, end point creation and app integration.
System Evolution: Drive the continuous improvement and evolution of data and AI architectures to meet emerging business needs, technological advancements, and industry trends.
Collaboration & Leadership: Work closely with delivery teams, data engineers, data scientists, software engineers, and IT operations to implement comprehensive data architectures that support AI and analytics initiatives focused on continuous improvement.
Strategic Vision: Partner with business and technology stakeholders to understand long-term goals, translating them into architectural frameworks and roadmaps that drive business value.
Governance & Best Practices: Ensure best practices in data governance, security, and compliance, overseeing the implementation of standards across AI and data systems.
Performance Optimization: Identify opportunities to optimize performance, cost-efficiency, and operational effectiveness of AI and data systems including ETL, ELT and data pipeline creation and evolution and optimizing of AI resource models.
Functional Knowledge.
Experience: 10+ years of experience in data architecture, AI systems, or cloud infrastructure, with at least 3-5 years in a leadership role.
Proven experience driving solutions from ideation to delivery and support.
Cloud Expertise: Deep hands-on experience with cloud platforms like AWS, Google Cloud Platform (GCP), and Databricks.
Familiarity with other data and AI platforms is a plus.
CRM Expertise: Hands-on experience with key CRM systems like Salesforce and AI systems inside of those solutions (ex.
Einstein).
AI & Analytics Systems: Proven experience designing architectures for AI, machine learning, analytics, and large-scale data processing systems.
Technical Knowledge: Expertise in data architecture, including data lakes, data warehouses, real-time data streaming, and batch processing frameworks.
Cross-Platform Knowledge: Solid understanding of containerization (Docker, Kubernetes), infrastructure as code (Terraform, CloudFormation), and big data ecosystems (Spark, Hadoop).
Experience in applying Agile methodologies, including Scrum, Kanban or SAFe.
Experience in top reporting solutions, including/preferred Tableau which is one of our cornerstone reporting solutions.
Leadership: Strong leadership and communication skills, with the ability to drive architecture initiatives in a collaborative and fast-paced environment.
Excellent problem solving skills and a proactive mindset.
Education: Bachelors degree in Computer Science, Data Science, or related field.
Masters degree or relevant certifications (e g, AWS Certified Solutions Architect) is preferred.
Business Expertise.
Experience in industries such as manufacturing, agriculture, or supply chain, particularly in AI and data use cases.
Familiarity with regulatory requirements related to data governance and security.
Experience with emerging technologies like edge computing, IoT, and AI/ML automation tools.
Your Experience And Qualifications.
Excellent communication / interpersonal skills, capable of interacting with multiple levels of IT and business management/leadership.
Hands on experience with SAP Hana, SAP Data Services or similar data storage, warehousing and/or ETL solutions.
10+ years of progressive IT experience.
Experience creating data models, querying data, business process and technical process mapping.
Successfully influences diverse groups and teams in a complex, ambiguous and rapidly changing environment to deliver value-added solutions.
Effective working relationship with the business to ensure business requirements are accurately captured, agreed, and accepted.
Adaptable to new technologies/practices and acts as change agent within teams.