IT Program Manager, Staff - Enterprise Data Transformation

Qualcomm•San Diego, CA

13d•$169,200 - $253,800•Onsite

About The Position

The Staff IT Program Manager will lead the planning and execution of Qualcomm’s enterprise data transformation, a multi-year initiative to establish the Enterprise Data Hub (EDH) as a unified, trusted, scalable, and governed foundation for enterprise data products, analytics, reporting, applications, and advanced technology solutions. The role will coordinate business and technology workstreams spanning data strategy, architecture, Databricks platform development, data engineering, data integration, migration, governance, quality, security, analytics, organizational change, adoption, and legacy-system retirement. The Program Manager will establish integrated governance, roadmaps, plans, budgets, dependencies, decision forums, and outcome measures; direct delivery across internal teams, software providers, systems integrators, and business functions; and provide transparent reporting to executive sponsors and steering committees. The role will ensure that enterprise data capabilities align with and support major business platforms and transformations, including CRM, HCM, ERP, PLM, supply chain, finance, customer, product, engineering, and other systems of record. This role reports to the Office of Planning & Delivery (OPD), part of the Office of the CIO (OCIO).

Requirements

Bachelor’s degree in Information Technology, Information Systems, Computer Science, Data Science, Engineering, Business, or a related field.
8+ years of program or project management experience, including leadership of large, complex, global technology or business transformation programs.
Demonstrated experience leading a significant enterprise data platform, data modernization, analytics, cloud, or related transformation through multiple lifecycle phases.
Program leadership experience with modern cloud data platforms and technologies such as Databricks, data lakehouse architectures, cloud data services, data integration, data engineering, data warehousing, business intelligence, or analytics platforms.
Experience coordinating work across data platform, architecture, engineering, governance, security, analytics, integration, testing, deployment, operations, and organizational change teams.
Strong understanding of enterprise data-management disciplines, including data architecture, data modeling, ingestion and transformation, data products, data quality, metadata, lineage, governance, security, master or primary data, semantic layers, and lifecycle management.
Experience integrating data from major enterprise applications such as CRM, HCM, ERP, PLM, finance, supply chain, customer, product, engineering, or other systems of record.
Experience leading data migration and modernization efforts involving legacy data warehouses, data pipelines, reports, dashboards, analytical workloads, or related data assets.
Experience managing multi-million-dollar budgets, business cases, systems integrators, software vendors, contracts, statements of work, and geographically distributed teams.
Ability to communicate with and influence executives, business data owners, product owners, architects, engineers, analysts, data scientists, security teams, finance partners, and external providers in a matrixed environment.
Proven ability to create structure in ambiguity, manage complex dependencies, drive difficult decisions, and deliver measurable business and technology outcomes.

Nice To Haves

10+ years of program management experience, including leadership of a multi-year, global enterprise data, analytics, cloud, or related transformation.
Hands-on program leadership experience developing or scaling an enterprise data hub, data lakehouse, data mesh, data-product ecosystem, or cloud data platform.
Experience with Databricks capabilities such as Delta Lake, Unity Catalog, Lakeflow, Databricks SQL, MLflow, AI/BI, or related data engineering, governance, analytics, and platform services.
Experience with public-cloud data services and architecture on AWS, Microsoft Azure, or Google Cloud Platform.
Experience implementing medallion architecture, reusable data products, semantic layers, business metrics, common data models, metadata management, data marketplaces, knowledge graphs, or governed self-service capabilities.
Experience establishing federated or hybrid data governance, including ownership, stewardship, certification, quality, lineage, access, privacy, security, and lifecycle policies.
Experience integrating data from CRM, HCM, ERP, PLM, supply chain, finance, customer, product, or engineering platforms such as Salesforce, Workday, Oracle, SAP, Siemens Teamcenter, or comparable enterprise solutions.
Experience modernizing or retiring legacy data warehouses, ETL platforms, business intelligence tools, dashboards, reports, and data pipelines while maintaining business continuity and stakeholder trust.
Technical fluency sufficient to evaluate data architecture, integration, engineering, security, performance, and operating-model tradeoffs with senior architects and engineers; working knowledge of SQL, Python, or comparable data-analysis techniques is beneficial.
Experience developing or enabling AI and machine-learning solutions that depend on governed enterprise data, including generative AI, retrieval-augmented generation, conversational analytics, intelligent agents, model operations, or intelligent automation.
Working knowledge of AI-ready data practices, including data quality, metadata, lineage, semantic context, security, privacy, responsible use, model and agent governance, and monitoring.
Demonstrated ability to identify valuable AI-enabled opportunities, partner with AI platform and solution teams, and translate business needs into scalable data and technology capabilities.
Experience in the semiconductor, high-technology, electronics, manufacturing, or similarly complex global industry.
Experience establishing transformation governance and presenting recommendations, architecture choices, tradeoffs, program health, and value realization to senior executives and steering committees.
Demonstrated organizational change management, adoption, data literacy, and business enablement experience for large-scale data and technology change.
PMP, PgMP, Databricks, cloud, data management, Prosci, SAFe, Scrum, or related certification.
Master’s degree in Business Administration, Information Systems, Computer Science, Data Science, Engineering, or a related field.

Responsibilities

Leads the full lifecycle of a large, global, cross-functional enterprise data transformation from strategy and business case through platform development, data onboarding, migration, solution delivery, deployment, adoption, operationalization, and value realization.
Builds and maintains an integrated, outcome-based transformation roadmap and plan of record covering EDH platform evolution, Databricks capabilities, data architecture, data engineering, data products, integrations, governance, security, analytics, migration, legacy retirement, organizational change, and operational readiness.
Establishes the program governance model, decision rights, workstream structure, delivery methodology, and executive reporting cadence; facilitates steering committee, program leadership, architecture, data governance, dependency, risk, and decision forums.
Partners with executive sponsors, business data owners, product owners, enterprise architects, and IT leaders across finance, sales, supply chain, manufacturing, human resources, product development, engineering, commercial operations, security, infrastructure, analytics, and other functions to define the transformation vision, scope, business outcomes, delivery priorities, and accountabilities.
Directs the development and evolution of EDH as the enterprise platform for governed data products, business intelligence, self-service analytics, enterprise applications, data sharing, and advanced analytical capabilities.
Coordinates Databricks platform strategy and implementation, including lakehouse and medallion architecture, scalable ingestion and transformation pipelines, batch and streaming patterns, data sharing, workspace and environment strategy, observability, performance, cost management, release management, and platform operations.
Drives enterprise data architecture and design decisions involving common data models, primary and master data, metadata, semantic layers, lineage, data contracts, retention, lifecycle management, interoperability, and secure access.
Ensures data governance is embedded throughout the transformation, including data ownership and stewardship, quality and certification, cataloging, lineage, privacy, security, regulatory compliance, retention, and auditable controls.
Maintains an end-to-end view of critical enterprise data flows, including data origination, transformation, enrichment, distribution, consumption, and control points across systems.
Leads data discovery and intake processes that translate business needs, pain points, analytical requirements, and strategic priorities into defined data products, use cases, and platform capabilities.
Establishes data classification and certification practices, including heightened quality, security, reconciliation, and approval requirements for critical, regulated, financial-reporting, and executive-decision datasets.
Partners with architecture and engineering leaders to establish reference architectures, reusable patterns, engineering standards, and platform guardrails that improve scalability, interoperability, reliability, security, and delivery speed.
Promotes DataOps and software-engineering practices across the platform, including version control, CI/CD, automated data testing, environment promotion, deployment controls, documentation, and production observability.
Defines and monitors service-level objectives and operational measures for platform availability, pipeline reliability, data freshness, processing performance, incident response, recovery, and stakeholder support.
Coordinates data and integration requirements across enterprise solutions such as CRM, HCM, ERP, PLM, finance, supply chain, customer, product, engineering, and related systems of record, ensuring alignment on data ownership, models, interfaces, timing, controls, and downstream consumption.
Leads the migration of data assets, pipelines, reports, dashboards, and analytical workloads from legacy data warehouses and platforms to EDH, including dependency analysis, sequencing, mapping, reconciliation, parity validation, business transition, retirement planning, and realization of operating-cost reductions.
Leads the strategy and execution of enterprise business intelligence rationalization, including assessment of existing reports, dashboards, data extracts, analytical applications, and tools; establishes criteria for retention, consolidation, redesign, migration, certification, and retirement; and partners with business owners to simplify the analytics landscape, improve trust and usability, reduce duplication and cost, and increase adoption of governed enterprise data products and standard BI capabilities.
Drives the development and adoption of reusable, domain-aligned data products that provide trusted business definitions, certified metrics, consistent dimensions, appropriate controls, and secure consumption across reporting, analytics, applications, and other enterprise use cases.
Partners with business and technology leaders to establish data-domain ownership, product-management practices, delivery priorities, service expectations, and measurable outcomes for enterprise data products.
Manages interdependencies across platform, domain, governance, analytics, integration, security, infrastructure, and enterprise-application workstreams and owns consolidated scope, schedule, budget, resources, quality, risks, issues, assumptions, dependencies, milestones, changes, decisions, and actions.
Directs integrated data and solution readiness, including source-to-target mapping, data profiling, cleansing, reconciliation, data quality thresholds, lineage, access controls, testing, performance validation, business acceptance, deployment, cutover, stabilization, and operational handoff.
Establishes consistent delivery practices for enterprise data initiatives, including intake, prioritization, estimation, planning, dependency management, architecture review, release management, status reporting, escalation, and benefits tracking.
Integrates stakeholder engagement, communications, data literacy, role-based training, organizational readiness, adoption, and sustained changes in business and technology practices into the transformation plan.
Establishes an enablement model for data engineers, analysts, data scientists, data stewards, product owners, power users, and business consumers, including reusable patterns, communities of practice, training, office hours, documentation, and adoption support.
Leads the transition to a sustainable data-product and platform operating model with clear ownership, governance, service management, support processes, financial management, performance measures, and continuous-improvement mechanisms.
Directs systems integrators, software providers, consultants, and other partners; coordinates workforce planning and reviews statements of work, staffing models, estimates, deliverables, acceptance criteria, commercial commitments, invoices, performance, and change requests.
Establishes the benefits-realization framework by defining financial and non-financial baselines, target outcomes, measures, timing, and accountable business owners; monitors value throughout delivery and ensures ongoing tracking of adoption, data quality, data trust, platform usage, delivery speed, legacy retirement, productivity, and business impact.
Anticipates cross-workstream risks and resource constraints, escalates decision needs early, drives mitigation and recovery plans, and provides concise, fact-based executive communications on program health, options, tradeoffs, financial outlook, adoption, and business outcomes.
Applies lessons learned and leading practices from prior data, cloud, analytics, and enterprise-application transformations and operates with substantial independence, executive presence, sound judgment, and the ability to influence outcomes without direct authority.