Client Introduction
Gilead Sciences, Inc. is a research-based biopharmaceutical company that has pursued breakthroughs in medicine for more than three decades. Headquartered in Foster City, California, Gilead operates in more than 35 countries worldwide and is committed to advancing innovative medicines to prevent and treat life-threatening diseases, including HIV, viral hepatitis, COVID-19, cancer, and inflammation.
Gilead’s portfolio spans virology, oncology, and inflammation, with flagship medicines including Biktarvy, Trodelvy, and Yeztugo — the world’s first twice-yearly HIV prevention option approved in 2025. In 2024, Gilead reported total revenue of $28.75 billion and has announced a planned $32 billion investment in US operations through 2030. With approximately 18,000 employees and a complex global manufacturing and supply chain network, Gilead requires robust enterprise-grade data and analytics infrastructure to support operational decision-making, regulatory compliance, and continuous performance improvement at scale.
Project Introduction
Project Name: Enterprise Data Warehouse and Business Intelligence Modernisation
Department: DevOps / Data & Analytics Engineering
Role: DevOps Engineer – Design, Development, Delivery and Maintenance
This engagement covered the end-to-end design, development, delivery, and ongoing maintenance of Gilead’s Enterprise Data Warehouse (EDW) and a suite of custom Business Intelligence applications serving as the analytical backbone for critical operational and financial functions across Gilead’s global business.
Scope of work includes:
- Architecture, design, and development of the Enterprise Data Warehouse
- Build and delivery of custom BI applications across Manufacturing, Supply Chain, Finance, and HR
- Ongoing DevOps support including maintenance, deployment pipelines, and continuous improvement
- Integration of data from manufacturing systems, ERP platforms, HR systems, and financial applications
- Enablement of self-service analytics through Data Virtualisation for Finance and HR teams
Key Applications
The programme delivered two primary analytics domains: Manufacturing & Supply Chain, and Finance & HR. Each encompassed purpose-built applications designed to serve the specific decision-making needs of Gilead’s operational and corporate functions.
Manufacturing & Supply Chain Analytics
Material Analysis & Shortage — Visibility into raw material availability, consumption patterns, and shortage risks enabling proactive procurement decisions.
Schedule Adherence — Production performance monitoring against planned schedules across manufacturing sites, identifying root causes of slippage and improving planning accuracy.
Abnormality Reporting — Structured capture, categorisation, and tracking of manufacturing deviations supporting quality management and regulatory audit readiness.
Lot Genealogy & End-to-End Product Visualisation — Full traceability of pharmaceutical lots from raw material sourcing to finished product release — critical for regulatory investigations, recalls, and quality improvement.
Supply Chain & Finance / HR Analytics
Inventory On Hand — Real-time and near-real-time visibility into inventory positions across global warehousing and distribution, supporting supply planning and working capital management.
On Time In Full (OTIF) — Delivery performance tracking against commitments, measuring fulfilment reliability and identifying service level gaps across commercial and logistics teams.
Site & Safety Metrics — Consolidated site-level operational performance and safety reporting supporting proactive risk management and compliance across Gilead’s manufacturing footprint.
Self-Service Analytics via Data Virtualisation — A unified, governed data layer for Finance and HR teams enabling ad hoc queries across multiple source systems without physical data movement, significantly reducing time-to-insight.
Technical Implementation
EDW Architecture
- Unified data integration: ETL and ELT pipelines consolidating data from manufacturing, ERP, HR, and financial source systems
- Governed data model: Structured, documented data model supporting reliable reporting across all business domains
- Scalable infrastructure: Designed to scale with Gilead’s growing data volumes, new manufacturing sites, and expanding analytics requirements
- Data Virtualisation layer: Abstraction layer enabling Finance and HR users to access governed data through a single interface without physical data movement
- DevOps delivery model: Continuous integration and deployment pipelines supporting reliable, repeatable delivery of application updates
DevOps Delivery Model
- Continuous integration and delivery: Automated pipelines ensuring application changes are tested, validated, and deployed consistently across environments
- Iterative development: Applications built and enhanced incrementally, allowing business requirements to be incorporated progressively
- Ongoing maintenance: Post-deployment support and maintenance ensuring the platform remains performant, accurate, and aligned with evolving business needs
- Cross-functional collaboration: Close working relationships between engineering, data, and business teams improving delivery speed and solution quality
Business Impact
Operational visibility
Unified analytics platform giving manufacturing, supply chain, and operations teams real-time access to performance data across Gilead’s global network.
Supply chain resilience
Material shortage analysis and inventory visibility allow supply chain teams to identify and respond to risks before they impact production.
Regulatory compliance
Lot Genealogy, Abnormality Reporting, and Safety Metrics directly support Gilead’s pharmaceutical manufacturing obligations and audit-ready reporting.
Finance & HR empowerment
Self-service analytics via Data Virtualisation reduces time-to-insight, enabling business users to answer questions independently without waiting for IT-delivered reports.
Appendix: Glossary of Terms
Abnormality Reporting: Structured documentation of deviations from standard manufacturing procedures for quality management and regulatory compliance.
Business Intelligence (BI): Data analysis tools, dashboards, and reporting applications transforming raw data into actionable business insights.
Data Virtualisation: Data integration creating a unified logical view of multiple source systems without physically moving or replicating the data.
DevOps: Practices combining software development and IT operations to shorten delivery cycles and enable continuous improvement through automation.
ELT / ETL: Data integration patterns for extracting, transforming, and loading data between systems for analytical use.
Enterprise Data Warehouse (EDW): A centralised repository consolidating data from multiple source systems, providing a single source of truth for analytics and BI.
ERP (Enterprise Resource Planning): Integrated software systems managing core business processes including procurement, manufacturing, finance, and HR.
Lot Genealogy: The complete traceability record of a pharmaceutical lot from raw material receipt through to finished product release.
On Time In Full (OTIF): A supply chain metric measuring the percentage of orders delivered both on time and in the correct quantity.
Schedule Adherence: A manufacturing performance metric measuring how closely actual production aligns with the planned schedule.
Self-Service Analytics: An approach enabling business users to independently query, explore, and visualise data without requiring IT or data engineering support.
Ready to Modernise Your Data and Analytics Platform?
Are you looking to unify your manufacturing, supply chain, or finance data into a scalable, governed analytics platform? Get in touch to explore how Enterprise Data Warehouse modernisation and custom BI applications can accelerate decision-making across your organisation.
Pramod Siddabattuni - Product Consultant / Data Solutions Architect 