Senior Data Integration Analyst
Opella Healthcare Group (company site, opens in a new tab)3+ yrsHyderabadOn-siteDirect career pageEst. 10–18 LPA
Posted 11h ago
Reach people at Opella Healthcare Group
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Opella Healthcare Group you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own the quality and timely delivery of clinical study data integration deliverables, including file transfer specifications, data transfer agreements, data mappings, implementation and testing.
- Coordinate clinical data flows between external providers and internal study stakeholders across sources such as central labs, eCOA, IRT, connected devices and telemedicine.
- Monitor integration risks and resolve issues through study conduct; propose technical solutions and contribute to process improvements and support for newer analysts.
Minimum Requirements
- Work Experience Required: At least 3 years in clinical data management and more than 1 year in external data integration and mapping.
- Strong experience with clinical data management and related regulations, including ICH E6 and E8; experience with CDISC standards and clinical data terminologies.
- Good knowledge of data transfer and data mapping using digital tools; strong written and verbal English for global communication.
- Bachelor’s or Master’s degree in Science or above, preferably in life sciences, mathematics, computer science or a similar technical field.
Ideal Candidate Profile
- Has hands-on clinical data integration experience and can coordinate specifications, implementation and testing across varied external data sources and study teams.
- Uses a quality- and risk-based approach to protect data accuracy, anticipate delivery bottlenecks and drive issues through to resolution.
- Exposure to advanced drug-development concepts such as decentralized trials, master and adaptive protocols, or eSource is useful; SAS, R, Python or AI-based automation is a plus.