Nivedha Sampath’s nine-year journey from large-scale patient data migrations to leading global compliance infrastructure modernization in the pharmaceutical industry
More than 25% of FDA warning letters since 2019 cite data accuracy issues, creating a crisis that costs pharmaceutical companies millions in remediation and lost productivity. While most organizations struggle to balance innovation with compliance, a select group of platform engineers has turned regulatory requirements into competitive advantages through strategic infrastructure modernization.
Nivedha Sampath stands at the forefront of this transformation. With nine years spanning Accenture’s 2 million patient record migrations, IQVIA’s zero-SLA-breach pharmacovigilance platforms, and now Takeda’s multi-regional compliance infrastructure, she has become one of the industry’s most sought-after experts in pharmaceutical data governance. Her technical documentation on Oracle Argus Safety module issues didn’t just solve internal challenges — the documentation was shared with Oracle support channels as a reference for broader implementation. At Takeda, Sampath led key initiatives that modernized the company’s global cloud and AI infrastructure.
In this exclusive interview, Sampath shares specific strategies for navigating FDA compliance requirements, explains why DevOps practices actually enhance rather than complicate regulatory adherence, and reveals how platform engineers can transform data governance from a cost center into a strategic advantage. Her insights come from direct experience managing systems that handle millions of patient records while maintaining zero compliance breaches — practical knowledge increasingly critical as pharmaceutical companies face intensifying regulatory scrutiny in 2025.
FDA warning letters frequently cite data accuracy problems in pharmaceutical companies. From your platform engineering perspective, what causes these data integrity failures?
Many issues stem from disconnected systems that don’t communicate properly, different departments using separate databases and manual processes to transfer information between them. Oracle Argus Safety, which I worked with extensively at IQVIA, requires precise data handling for regulatory submissions to authorities like the FDA, EMEA, and PMDA. During my time there, I authored technical documentation that addressed these specific module issues and workaround strategies. The challenge isn’t just technical – it’s organizational. Companies often have robust systems but lack the automated workflows to ensure data consistency across all touchpoints.
Your experience spans multiple pharmaceutical companies: Accenture, IQVIA, and Takeda. How do regulatory requirements differ between organizations, and what common patterns do you see?
At Accenture, I handled ETL processes for over 2 million patient records, where regulatory compliance focused heavily on data migration accuracy and post-implementation monitoring. IQVIA required 24/7 support with zero SLA breaches for pharmacovigilance platforms, emphasizing continuous availability for regulatory reporting periods. Takeda operates in a more complex multi-regional environment, requiring compliance across US, European, and Asian jurisdictions simultaneously. Common patterns include the need for comprehensive audit trails, role-based access controls, and automated backup procedures. All three organizations struggled with balancing innovation against regulatory constraints, particularly when implementing new cloud technologies.
Platform engineers often need to work with both technical teams and regulatory affairs. How do you translate complex cloud architecture decisions into compliance language?
I’ve learned to frame technical discussions around business outcomes rather than implementation details. When proposing the KPI dashboard in Qlik for leadership reporting on voluntary turnover metrics, I focused on data accuracy improvements and audit trail capabilities rather than the underlying BDM Informatica integration. Regulatory teams care about demonstrable controls, documented procedures, and measurable outcomes. For my Scientific Computing Platform design using EC2, I emphasized automated compliance checks, quota management, and resource scheduling rather than the technical architecture. Successful projects require showing how technical solutions directly address regulatory requirements while improving operational efficiency.
You’ve implemented CI/CD pipelines and Infrastructure-as-Code practices. How do these DevOps approaches help or complicate regulatory compliance?
DevOps practices actually enhance compliance when implemented correctly. Infrastructure-as-Code using Terraform and CloudFormation provides the documented, repeatable processes that regulators expect. Every infrastructure change becomes part of an auditable version control system with clear approval workflows. I provided technical leadership in CI/CD pipelines, including code review processes that ensure data quality standards and scalability. The challenge comes during implementation; regulatory teams initially worry about automation reducing human oversight. However, automated testing and deployment actually minimize human error while creating comprehensive audit logs that manual processes can’t match.
Looking at your work with multiple AI/ML platforms like DataRobot, how are regulatory expectations evolving around artificial intelligence in pharmaceutical operations?
Regulators demand explainable AI with clear documentation of training data, model validation, and decision processes. My implementation of AutoML platforms required establishing governance frameworks around model development, testing, and deployment. DataRobot integration needs documented workflows showing how models are validated against known datasets and how predictions are reviewed by qualified personnel. The key insight is that AI doesn’t eliminate regulatory requirements – it requires additional documentation around model governance, bias detection, and outcome validation. Successful AI implementation in pharmaceuticals requires treating algorithms as regulated processes subject to the same change control and validation requirements as any other critical system.
You’ve worked on migrating from legacy Hive metastore to Databricks Unity Catalog. What specific compliance challenges does this type of migration present?
Legacy systems often lack the granular access controls and audit capabilities that modern regulations demand. Unity Catalog provides centralized data governance across AWS accounts and regions, but the migration process requires maintaining service availability while implementing new security frameworks. I implemented Delta Sharing as both a distribution tool and disaster recovery strategy, which addressed regulatory requirements for data continuity. The migration dealt with complex issues like data lineage tracking, access control inheritance, and maintaining services during the transition. Every change needed to be documented with approval workflows and rollback procedures to satisfy regulatory audit requirements.
Based on your experience implementing cross-functional platforms serving multiple global teams, what advice would you give other platform engineers facing similar regulatory challenges?
Start with understanding the regulatory context before designing technical solutions. My work supporting multiple global manufacturing teams required learning how each department’s regulatory requirements affected their data access patterns and approval workflows. Build compliance into the architecture from the beginning rather than adding it later: retrofitting security and audit capabilities is exponentially more difficult. Documentation is equally important as implementation; regulators review processes and procedures as thoroughly as they examine technical controls. Finally, establish clear communication channels between technical teams and regulatory affairs early in any project. The best technical solution means nothing if it doesn’t meet compliance requirements or can’t be explained clearly during regulatory inspections.
