Key Responsibilities 1. Clinical Data Platform Leadership

- Lead the engineering strategy and execution for Clinical Data Platforms supporting Clinical Operations, Data Management, Biostatistics, Medical Affairs, and Regulatory functions.
- Drive modernization of enterprise clinical data ecosystems using cloud-native architectures and Data Product principles.
- Establish reusable clinical data services, ingestion frameworks, and standardized data models.
- Partner with Clinical Development stakeholders to define technology roadmaps and prioritize platform investments.
- Ensure scalability, performance, data quality, security, and operational excellence of clinical data solutions.

1. Data Engineering & Data Products

- Lead the design and implementation of enterprise-scale Clinical Data Lakehouse solutions using Databricks and modern cloud technologies.
- Build and optimize ETL/ELT pipelines integrating diverse data sources, including:
- Clinical Trial Management Systems (CTMS)
- Electronic Data Capture (EDC)
- ePRO / eCOA platforms
- Safety & Pharmacovigilance systems
- Regulatory data sources
- Real-World Data (RWD)
- Medical Affairs data platforms

- Develop reusable clinical data products supporting analytics, reporting, AI, and self-service consumption.
- Drive best practices for:

- Data governance
- Metadata management
- Data lineage
- Data quality
- Data interoperability

1. Artificial Intelligence & Generative AI

- Lead implementation of AI-powered solutions to improve clinical trial efficiency, data review, patient insights, and operational decision-making.
- Develop and operationalize:
- Machine Learning solutions
- Predictive Analytics
- Generative AI applications
- Agentic AI platforms
- Clinical Copilots
- Intelligent Automation solutions
- Retrieval-Augmented Generation (RAG) architectures

- Establish AI governance frameworks aligned with Responsible AI principles and regulatory expectations.
- Partner with Data Scientists and Product Teams to operationalize AI solutions using MLOps best practices.
- Drive AI platform adoption across Clinical Development and R&D; functions.

1. Product Delivery & Stakeholder Engagement

- Lead Agile delivery teams utilizing Product Operating Model principles.
- Collaborate closely with:
- Clinical Scientists
- Data Managers
- Biostatistics Teams
- Medical Affairs




- Regulatory Affairs
- Product Managers
- Enterprise Architects

- Translate complex business and scientific requirements into scalable technology solutions.
- Lead architecture reviews, technical design decisions, project execution, and risk mitigation activities.
- Communicate effectively with executive leadership and business stakeholders.

1. People Leadership

- Build, develop, and retain high-performing Data Engineering and AI teams.
- Establish engineering excellence practices, technical standards, and career development frameworks.
- Foster a culture of:
- Innovation
- Accountability
- Collaboration
- Continuous improvement

- Lead workforce planning, vendor management, hiring, and capability development activities.

1. Compliance, Security & Quality

- Ensure compliance with:
- GxP
- Computer System Validation (CSV)
- HIPAA
- GDPR
- J&J; Data Privacy Standards
- AI Governance requirements

- Implement and maintain secure, compliant, and auditable platforms within regulated environments.
- Partner with Quality, Compliance, and Security teams to ensure inspection and audit readiness.

Required Qualifications Education
- Bachelors Degree in:
- Computer Science
- Engineering
- Information Technology
- Data Science
- Bioinformatics
- or a related field

- Masters Degree preferred.

Professional Experience
- 10+ years of experience in Data Engineering, Data Platforms, Analytics, or Artificial Intelligence.
- 5+ years of people leadership experience, preferably managing global engineering teams.
- Experience delivering enterprise-scale cloud data platforms and up-to-date analytics capabilities.
- Proven experience implementing AI/ML and Generative AI solutions in production environments.
- Experience working within Pharmaceutical, Biotechnology, Healthcare, Life Sciences, or other regulated industries.
- Demonstrated success managing cross-functional stakeholders and global delivery teams.





Technical Skills Data Engineering
- Databricks
- Apache Spark
- Delta Lake
- SQL
- Python
- Data Modeling
- ETL / ELT Frameworks
- Data Lakehouse Architecture
- Data Product Development

Cloud & Platform Engineering
- Azure - Preferred
- AWS
- Cloud-native Data & AI Services
- Infrastructure as Code (IaC)
- DevOps
- CI/CD

Artificial Intelligence & Machine Learning
- Machine Learning Engineering
- Large Language Models (LLMs)
- Generative AI
- Agentic AI
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- MLOps
- AI Governance
- Predictive Analytics
- Intelligent Automation

Clinical Domain Knowledge Candidates should have knowledge or experience in one or more of the following:
- Clinical Trial Data
- Clinical Data Management
- CDISC Standards
- SDTM
- ADaM

- Regulatory Data
- Medical Affairs Data
- Safety & Pharmacovigilance
- Real-World Data (RWD)
- Real-World Evidence (RWE)
- Clinical Analytics & Insights

Preferred Qualifications
- Experience supporting Clinical Development, Clinical Data Management, or Pharmaceutical R&D; organizations.
- Databricks, Azure, or AWS certifications.
- Experience building AI-enabled products and data platforms.
- Knowledge of Product Operating Model and Data Mesh principles.
- Experience leading digital transformation initiatives within Healthcare or Life Sciences organizations.
- Experience working in highly regulated environments with strong understanding of data privacy, security, quality, and compliance requirements.

Leadership & Behavioral Competencies The ideal candidate should demonstrate:
- Strong people and engineering leadership
- Strategic thinking and technology vision
- Product-oriented mindset
- Strong stakeholder management
- Excellent communication and influencing skills
- Ability to work with global and cross-functional teams
- Strong problem-solving and decision-making capabilities
- Ownership and accountability
- Innovation mindset
- Ability to operate effectively in complex, regulated environments

Disclaimer: This has been sourced from a public domain and may have been modified by Naukri.com to improve clarity for our users. We encourage job seekers to verify all details directly with the employer via their official channels before applying.

📌 Mgr, Forward Deployed Engineer, Data & Intelligence (Hyderabad)
🏢 Agile Technology
📍 Hyderabad