IT Program Manager, Staff - Enterprise Data Transformation

Technology, Data & Digital · Data, AI & Analytics · Data Engineering · Business Intelligence · Data Analysis

In short

The Staff IT Program Manager will lead Qualcomm's multi-year enterprise data transformation initiative to establish the Enterprise Data Hub (EDH) as a unified data foundation. This role involves coordinating business and technology workstreams across data strategy, platform development, data engineering, governance, and migration, reporting to the Office of Planning & Delivery. The program manager will partner with executive sponsors and various IT and business leaders to ensure enterprise data capabilities align with strategic business objectives.

Responsibilities

Lead the full lifecycle of a large, global, cross-functional enterprise data transformation from strategy through operationalization and value realization.
Build and maintain an integrated, outcome-based transformation roadmap and plan covering EDH platform evolution, Databricks capabilities, data architecture, data engineering, data products, integrations, governance, security, analytics, migration, and legacy retirement.
Establish program governance, decision rights, workstream structure, delivery methodology, and executive reporting cadence; facilitate steering committee, program leadership, architecture, data governance, risk, and decision forums.
Partner with executive sponsors, business data owners, product owners, enterprise architects, and IT leaders across various functions to define transformation vision, scope, business outcomes, and accountabilities.
Direct the development and evolution of EDH as the enterprise platform for governed data products, business intelligence, self-service analytics, and advanced analytical capabilities.
Coordinate Databricks platform strategy and implementation, including lakehouse and medallion architecture, scalable ingestion and transformation pipelines, and cost management.
Drive enterprise data architecture and design decisions involving common data models, metadata, semantic layers, lineage, and data contracts.
Ensure data governance is embedded throughout the transformation, including data ownership, quality, cataloging, lineage, privacy, security, and regulatory compliance.
Lead data discovery and intake processes that translate business needs into defined data products and platform capabilities.
Promote DataOps and software-engineering practices, including version control, CI/CD, automated data testing, and production observability.
Define and monitor service-level objectives and operational measures for platform availability, pipeline reliability, and data freshness.
Coordinate data and integration requirements across enterprise solutions such as CRM, HCM, ERP, PLM, finance, and supply chain.
Lead the migration of data assets, pipelines, reports, and analytical workloads from legacy platforms to EDH.
Lead the strategy and execution of enterprise business intelligence rationalization, and partner with business owners to simplify the analytics landscape.
Drive the development and adoption of reusable, domain-aligned data products that provide trusted business definitions and certified metrics.
Manage interdependencies across platform, domain, governance, analytics, integration, security, infrastructure, and enterprise-application workstreams.
Integrate stakeholder engagement, communications, data literacy, training, organizational readiness, adoption, and sustained changes into the transformation plan.
Lead the transition to a sustainable data-product and platform operating model with clear ownership, governance, and service management.
Direct systems integrators, software providers, consultants, and other partners; coordinate workforce planning and reviews.
Establish the benefits-realization framework by defining financial and non-financial baselines, target outcomes, and measures.
Anticipate cross-workstream risks and resource constraints, escalates decision needs early, and provides concise executive communications on program health.
Applies lessons learned and leading practices from prior data, cloud, analytics, and enterprise-application transformations.

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.

Desired Qualifications

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.

Benefits

Competitive annual discretionary bonus program.
Opportunity for annual RSU grants.
Highly competitive benefits package designed to support your success at work, at home, and at play.
Onsite work policy compliance.
US-based position.

Skills

DatabricksData LakehouseData EngineeringData IntegrationData WarehousingBusiness IntelligenceAnalyticsCRMHCMERPPLMSupply ChainSQLPythonAWSAzureGCPSalesforceWorkdaySAPETL

#IT Program Management#Enterprise Data Transformation#Data Hub#Databricks#Data Engineering#Data Integration#Data Governance#Analytics#Cloud Data Platforms#Digital Transformation