Architecture and Technology Governance

• Design and review end-to-end solution architectures spanning data, applications, integrations, analytics, and AI.
• Partner with enterprise architects and Architecture Review Boards to present, defend, and obtain approval for proposed technology solutions.
• Ensure architectures comply with enterprise standards for security, privacy, data governance, resiliency, scalability, and regulatory requirements.
• Lead technical design reviews and challenge engineering teams to develop pragmatic, maintainable, and cost-effective solutions.
• Drive the adoption of reusable architecture patterns, common services, APIs, engineering standards, and technology guardrails.
• Evaluate emerging technologies and provide recommendations based on business value, implementation complexity, risk, and total cost of ownership.

DevOps and Deployment Automation

• Provide hands-on leadership in DevOps using Azure DevOps and GitHub.
• Establish enterprise CI/CD standards across data platforms, applications, APIs, analytics solutions, infrastructure, and AI products.
• Drive full automation of build, testing, security scanning, infrastructure provisioning, deployment, validation, and rollback processes.
• Design and implement blue-green deployment strategies to minimize downtime and reduce production deployment risk.
• Implement infrastructure as code, automated quality gates, branching strategies, release controls, and environment-management practices.
• Establish deployment observability and automated rollback mechanisms to improve release reliability.
• Promote DevSecOps practices by embedding security, compliance, code quality, and vulnerability checks into delivery pipelines.

Data Governance and Strategy

• Define and implement enterprise data strategies aligned with business priorities and regulatory requirements.
• Establish data governance practices covering ownership, stewardship, classification, lineage, cataloging, access control, retention, and data quality.
• Partner with business and technology stakeholders to define data domains, data products, governance operating models, and accountability frameworks.
• Ensure governance controls are integrated into Databricks, Microsoft Fabric, Power BI, and the broader data ecosystem.
• Drive responsible data usage and support governance requirements for analytics and AI solutions.
• Establish measurable standards for data quality, platform adoption, reuse, performance, and business value.

People and Stakeholder Leadership

• Lead multidisciplinary teams across data engineering, software development, architecture, DevOps, QA, analytics, AI, and program management.
• Build an effective global delivery model with clear accountability, governance, escalation paths, and communication practices.
• Coach engineering and delivery leaders while remaining actively involved in key technical decisions.
• Partner with executive stakeholders to communicate program progress, investment needs, risks, dependencies, and business outcomes.
• Manage internal teams, strategic partners, and third-party vendors against defined delivery and quality expectations.
• Foster a culture of engineering excellence, continuous improvement, innovation, collaboration, and ownership.

Required Qualifications

• 15+ years of experience in technology delivery, engineering, architecture, or program leadership.
• 8+ years of experience leading large, complex enterprise technology or data programs.
• Proven experience managing globally distributed, multidisciplinary teams of 100 or more resources.
• Strong hands-on experience implementing enterprise data and analytics platforms using Databricks and Microsoft Fabric.
• Demonstrated ability to design and review enterprise-scale architectures and successfully partner with Architecture Review Boards.
• Strong experience with:
• Python
• .NET and C#
• React or Angular
• Microsoft Power BI
• Microsoft Fabric
• Databricks
• Artificial intelligence and generative AI technologies
• REST APIs and enterprise integration patterns
• Hands-on experience with Azure DevOps, GitHub, CI/CD pipeline engineering, and automated release management.
• Demonstrated experience implementing zero- or minimal-downtime deployment patterns, including blue-green deployments.
• Strong understanding of cloud architecture, distributed systems, data engineering, security, resiliency, performance, and observability.
• Practical experience defining and implementing data governance frameworks and enterprise data strategies.
• Experience managing program financials, resource planning, delivery metrics, dependencies, risks, and executive governance.
• Excellent executive communication, stakeholder management, negotiation, and problem-solving skills.
• Ability to move comfortably between executive-level discussions, architectural decisions, and detailed engineering reviews.

Preferred Qualifications

• Experience delivering data and AI platforms in highly regulated or security-sensitive enterprise environments.
• Knowledge of Azure-native services, cloud security, infrastructure as code, Kubernetes, and containerized deployment models.
• Experience with Databricks Unity Catalog, Microsoft Purview, Fabric OneLake, data lineage, and metadata management.
• Experience implementing MLOps, LLMOps, retrieval-augmented generation, AI agents, or enterprise generative AI solutions.
• Relevant certifications in Azure, Databricks, Microsoft Fabric, architecture, DevOps, Agile, or program management.
• Experience managing product-based engineering organizations and transitioning platforms from development into production operations.

Orion is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, citizenship status, disability status, genetic information, protected veteran status, or any other characteristic protected by law.

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