Senior Enterprise Cloud Data Architect

Benefits Overview

A career at CoBank can offer you the opportunity to make a personal impact on the people and communities where we do business. When you choose a career with CoBank, you make a difference by standing for something that matters. In order to be the best, we hire the best!

Remarkable Benefits Offered by CoBank

• Careers with a purpose. Stand for something!
• Time-Off Packages, 15 days of vacation, 10 paid sick days and 11 paid holidays
• Competitive Compensation & Incentive
• Hybrid work model: flexible arrangements for most positions
• Benefits Packages, including Medical, Dental and Vision coverage, Disability, AD&D, and Life Insurance
• Robust associate training and development with CoBank University
• Tuition reimbursement for higher education up to 10K
• Outstanding 401k: up to 6% matching and additional 3% non-elective contribution
• Community Impact: United Way Angel Day, Volunteer Day and Associate Directed Contribution
• Associate Resource Groups: creating a culture of respect and inclusion
• Recognize a fellow associate through our GEM awards

Job Description

The Senior Enterprise Data Architect is responsible for supporting CoBank's data architectural vision and roadmap, specializing in cloud-native architectures, microservices, distributed systems, and scalable data platforms. This role combines strategic leadership with hands-on technical implementation to deliver robust, secure, and efficient enterprise data solutions that align with business objectives and compliance requirements.

Essential Functions

1. Lead the Creation and Execution of Data Architecture Roadmaps: Partner with the Chief Architect to define and drive CoBank’s data architecture vision and roadmap. Ensure alignment with product and business objectives, compliance requirements, and long-term organizational strategy. 2. Design and Implement Scalable Data Architecture Solutions: Develop and deploy strategies for data lakes, data warehouses, ETL/ELT pipelines, real-time processing systems, and AI/ML pipelines, ensuring robust, scalable, and efficient data platforms. 3. Establish and Maintain Data Platform Engineering Best Practices: Build scalable, repeatable patterns that enhance developer experience and minimize friction for diverse consumers, including developers, data scientists, analysts, and business users. 4. Drive Data Architecture Review and Alignment: Facilitate focused, outcome-driven data architecture review sessions. Provide actionable recommendations and ensure alignment across cross-functional teams by curating and socializing Architecture Decision Records (ADRs). 5. Implement Data Governance and Quality Frameworks: Develop and enforce frameworks that balance security, compliance, and usability, driving enterprise-wide adoption while demonstrating measurable business value. 6. Collaborate on AI/ML Pipeline Development: Work closely with data scientists and ML engineers to design and implement scalable data pipelines, feature stores, training datasets, and real-time inferencing architectures optimized for AI/ML workloads. 7. Innovate with Emerging Technologies: Research and integrate cutting-edge technologies, including generative AI and advanced ML frameworks, into the enterprise data ecosystem to drive innovation and maintain competitive advantage. 8. Enable Observability and Monitoring: Architect and implement observability, monitoring, and quality control systems to ensure data reliability and transparency across the enterprise data ecosystem. 9. Foster Automation and Operational Excellence: Build and manage CI/CD pipelines and Infrastructure as Code (IaC) solutions, emphasizing automation, reproducibility, and operational efficiency for all solution architectures. 10. Provide Hands-On Technical Leadership: Actively participate in the implementation of features and proofs of concept (POCs) while mentoring teams, defining best practices, and ensuring the translation of conceptual designs into practical, executable solutions.

Education

• Bachelor's Degree in Computer Science, Engineering or a related field preferred

Work Experience

5 years Proven expertise in cloud-native data architectures, modern cloud platforms (AWS preferred), relational and NoSQL databases, enterprise data security practices, api development and Infrastructure as Code (IaC) tools with hands-on experience in programming languages like Python, Node.js, Java/Scala, or .NET and a strong track record of leading engineering teams to deliver scalable, secure solutions required 3 years Extensive experience in designing enterprise data architectures, including data lakes, data mesh, and data fabric solutions, with expertise in multi-dimensional data modeling (star and snowflake schemas), CI/CD pipelines, and observability tools