Application Support and Integration AnalystPosition Summary We are seeking an experienced Application Support and Integration Analyst to provide Level 2 production support for a laboratory sample request and tracking platform and its connected data systems.
This role will focus on troubleshooting data synchronization, API, authentication, timeout, search, and backend production issues across laboratory systems, databases, and integration platforms. The resource will also support QA and UAT test data setup, incident documentation, and root-cause analysis.
Key Responsibilities - Troubleshoot data synchronization issues across SaRA-Connect, RDL, and LIMS platforms, including Dotmatics, LabWare, and Benchling.
- Investigate API, authentication, and timeout issues.
- Support Elasticsearch catalog queries and index troubleshooting.
- Create and manage QA and UAT test data.
- Provide Level 2 production support for data and backend incidents.
- Troubleshoot cron jobs, database synchronization, and data movement issues.
- Review .NET service logs and trace exceptions across SaRA-Connect and related services.
- Use Splunk or similar tools for log analysis and incident investigation.
- Document incidents, findings, root causes, and resolution steps in Jira, Confluence, and ServiceNow.
- Provide production support coverage during U.S. business hours.
Required Skills and Experience - 3 to 5 or more years of experience supporting production data or integration platforms in an enterprise or regulated environment.
- Strong SQL experience across SQL Server, Redshift, and PostgreSQL.
- Familiarity with LIMS platforms such as LabWare, Dotmatics, or Benchling.
- Experience with data lake environments, including Redshift.
- Experience troubleshooting cron jobs, database synchronization, and backend data issues.
- Working knowledge of Elasticsearch.
- Ability to read .NET service logs and trace exceptions.
- Familiarity with EKS, Jenkins, and CI/CD processes.
- Experience using Splunk or a comparable log investigation tool.
- Experience with Jira, Confluence, and ServiceNow.
- Strong written communication and incident documentation skills.
- Life sciences experience is preferred.
AI-Assisted Troubleshooting - Use approved AI coding tools such as Claude Code, GitHub Copilot, or similar tools to support root-cause analysis.
- Use AI to help trace exceptions, correlate logs with source code, and analyze SQL or Splunk data.
- Use approved internal AI tools to retrieve relevant incident history and documentation.
- Validate all AI-assisted findings before taking action.
- Do not allow unreviewed AI actions against production systems.
- Document all AI-assisted troubleshooting steps in the incident ticket.
