Technical Delivery Manager

Role Overview

We are seeking an accomplished Senior Technical Delivery Manager to lead the architecture, engineering, and delivery of large-scale data, analytics, and AI platforms. This role requires a rare combination of deep technical expertise, enterprise architecture experience, and proven leadership of globally distributed, multidisciplinary teams.

The successful candidate will manage a global organization of approximately 150 resources across data engineering, software engineering, cloud, DevOps, quality engineering, architecture, analytics, AI, and program management. The individual must remain technically hands-on, confidently engage with engineering teams, and partner with enterprise Architecture Review Boards to drive scalable and secure technology decisions.

Technical Delivery and Program Leadership

• Lead the end-to-end delivery of complex, enterprise-scale data, analytics, application, and AI programs.
• Manage a globally distributed team of approximately 150 professionals across multiple disciplines, locations, vendors, and time zones.
• Establish program governance covering scope, schedule, budget, dependencies, quality, risks, resources, and executive reporting.
• Translate business objectives into actionable technology roadmaps, delivery plans, and measurable outcomes.
• Manage multiple concurrent workstreams while maintaining alignment across architecture, engineering, DevOps, security, testing, and operations.
• Drive delivery predictability through effective planning, dependency management, risk mitigation, and outcome-based performance metrics.
• Build strong relationships with senior business leaders, product owners, engineering leaders, and technology partners.

Data and Analytics Platform Engineering

• Provide hands-on technical leadership for large-scale implementations using Databricks and Microsoft Fabric.
• Design and deliver modern enterprise data platforms, including data lakes, lakehouses, warehouses, data pipelines, semantic models, and analytics solutions.
• Guide decisions related to platform architecture, workload design, performance, scalability, security, resiliency, and cost optimization.
• Lead the integration of Microsoft Fabric, Databricks, Power BI, enterprise applications, APIs, and AI capabilities.
• Apply practical knowledge of batch and real-time data processing, medallion architecture, Delta Lake, data modeling, and distributed computing.
• Establish engineering standards and reusable frameworks for data ingestion, transformation, orchestration, quality, observability, and consumption.

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.