Role OverviewWe 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.
Key ResponsibilitiesTechnical 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.
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.
