Note: The job is a remote job and is open to candidates in USA. Dexian provides talent and technology solutions through global staffing and professional services. The Sr. eDiscovery Data Collection Analyst will support complex legal matters across the discovery lifecycle, focusing on electronic data collection, processing, search, review, and production. The role partners with legal teams, manages multiple matters, troubleshoots technical issues, and supports eDiscovery process improvements.
Responsibilities
- Perform defensible collection of electronically stored information (ESI) across various systems, applications, devices, and custodians
- Partner with legal teams to determine collection scope, relevant data sources, custodians, and appropriate methodologies
- Process collected data and troubleshoot exceptions, errors, and technical issues
- Build, test, and refine searches to identify relevant information
- Prepare data and review batches for attorney review
- Coordinate document productions and privilege-related workflows
- Provide technical support and consultation for eDiscovery databases and litigation technology
- Maintain documentation related to collections, processing, and matter workflows
- Manage multiple matters and priorities in a deadline-driven environment
- Provide guidance and mentorship to junior team members
- Support improvements to internal eDiscovery processes, tools, and procedures
Skills
- 7+ years of eDiscovery, litigation support, or related experience
- Strong experience with ESI collection and data processing
- Knowledge of defensible collection and preservation practices
- Experience with search development, review workflows, and document productions
- Ability to troubleshoot technical and data-processing issues
- Strong communication skills and experience working directly with legal teams
- Experience managing multiple matters and competing deadlines
- Hands-on experience with (One or more) established eDiscovery, cloud collection, digital evidence, or forensic acquisition technologies is preferred
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