GitLab Performance Tool Setup Guide for Self-Managed HA

I need an experienced GitLab and DevOps engineer to guide me through setting up and running the GitLab Performance Tool (GPT) on a self-managed GitLab High Availability environment. This is a hands-on teaching engagement — I will perform every step myself while you guide me live over Google Meet. This is NOT a "do it for me" project. I want to learn the process thoroughly so I can run future performance tests independently. The right person has already done this multiple times and can explain every step clearly and confidently. Requirements: - Proven hands-on experience setting up GitLab Performance Tool (GPT) in self-managed HA environments - Familiarity with GitLab Ultimate (versions 14, 15, or 16) on Linux multi-node setups - Experience with GitLab Environment Toolkit and gpt-data-generator - Ability to troubleshoot Docker, environment variables, and dependency issues - Strong teaching and mentoring skills — you must be comfortable guiding rather than doing - Available for live sessions over Google Meet - Able to advise on alternative approaches (e.g., generating test data without local Docker) Deliverables: - Live guided sessions (Google Meet) walking me through the full GPT setup from scratch - Verification of prerequisites: GitLab version/edition, network/SSH access, and host resource requirements - Guided configuration of the data generator and test environment - Execution of a smoke test followed by a full performance test run - Review and explanation of test results - Clear documentation or notes I can reference for future independent runs Work Arrangement: This is an hourly engagement. Sessions will be scheduled based on mutual availability. Estimated hours will depend on environment complexity and pace of progress. About the project: This is a self-managed GitLab HA environment used for enterprise-level performance testing. The goal is to build internal capability — not just get the tool running once, but to fully understand the process end to end.

Project ID: 40546815