Data Manager II: Data Cleaning & UAT Lead

Altasciences; United States; On-site; USD 27,000 - 36,000; Full time

Benefits offered by this job

Health/Dental/Vision plans

401(k) with employer match

Paid vacation & holidays

Job summary

Altasciences is seeking a Data Manager II to support sponsors’ research programs by performing data cleaning and management activities across clinical datasets. You will ensure data integrity, track eCRFs, and run queries per protocol and ICH-GCP standards, while mentoring junior staff.

The role involves training CRAs and sponsor representatives on EDC usage, leading data validation efforts, and collaborating with multidisciplinary teams to improve data management efficiency and quality.

Qualifications

• Bachelor's degree required; relevant experience may substitute.
• Excellent verbal and written communication skills.
• Able to work with a multi-disciplinary team of professionals.
• Understanding of clinical research and the data management role.

Responsibilities

• Perform data management cleaning activities including listing review, queries, and vendor data reconciliation.
• Track eCRFs and queries, providing metrics as requested.
• Lead or participate in UAT for clinical databases.
• Provide EDC system training (via WebEx) for CRAs and sponsor reps.
• Mentor and train Data Management team members.

Skills

Data cleaning

eCRF tracking

UAT

Interdisciplinary collaboration

Clinical research understanding

Education

Bachelor's degree in programming, health-related sciences, or relevant field

Tools

EDC systems

WebEx

Job description

Altasciences is seeking a Data Manager II to support sponsors’ research programs by performing data cleaning and management activities across clinical datasets. You will ensure data integrity, track eCRFs, and run queries per protocol and ICH-GCP standards, while mentoring junior staff.

The role involves training CRAs and sponsor representatives on EDC usage, leading data validation efforts, and collaborating with multidisciplinary teams to improve data management efficiency and quality.