Release Engineer

Required Skills & Experience

Demonstrated experience managing software, data, analytics, or BI releases in a structured delivery environment. Hands-on experience with Git-based workflows, pull requests, branching, merge management, and release coordination. Experience with CI/CD or deployment pipelines and promotion across multiple environments. Working knowledge of Microsoft Fabric and/or Power BI artifacts, including notebooks, pipelines, semantic models, and reports. Strong attention to detail for release evidence, approvals, documentation, and traceability. Experience coordinating multiple concurrent changes and resolving release dependencies. Ability to communicate release status, risks, blockers, and recovery plans clearly to technical and non-technical stakeholders. Experience supporting production incidents and post-release verification.

Nice to Have Skills & Experience

Experience in pharmaceutical, biotechnology, clinical development, or another regulated enterprise environment. Experience with validation-aware or controlled SDLC practices, including testing, traceability, approvals, change control, and release evidence. Hands-on experience with Microsoft Fabric deployment pipelines, Azure DevOps or GitHub, Power BI/PBIP or equivalent artifact management. Experience supporting global teams across multiple time zones. Experience improving release processes through automation, standardization, and measurable operational controls.

Job Description

A global biopharmaceutical company is seeking an experienced Release Engineer to support controlled delivery of clinical analytics solutions built with Microsoft Fabric, Power BI, notebooks, pipelines, semantic models, and shared code. This role owns the mechanics and discipline of promotion through Dev, Test/UAT, and Production, while helping the team maintain reliable release records, evidence, and production handoffs. The successful candidate should be comfortable working in a multi-developer, globally distributed analytics environment with Git, pull requests, deployment pipelines, release calendars, production support, and validation-aware SDLC expectations. This position coordinates and executes releases but does not own clinical definitions, solution architecture, or semantic-model design. This is an ongoing, renewing contract opportunity that can sit remotely anywhere in LATAM as long as the individual is comfortable working US EST business hours. Key Responsibilities Execute controlled deployment-pipeline promotions across Dev, Test/UAT, and Production in accordance with approved release plans. Coordinate release readiness across Fabric notebooks, lakehouses, pipelines, semantic models, and Power BI reports. Verify prerequisites, approvals, test evidence, dependency completion, and required release documentation before promotion. Maintain release calendars, deployment records, release notes, implementation checklists, and post-release status. Coordinate rollback or recovery activities when a release does not behave as expected. Partner with developers, QA, PM/Delivery Manager, and technical leads to identify release risks early. Support production incident triage and ensure the correct technical owner is engaged quickly. Reinforce separation of development and promotion responsibilities and follow applicable controlled-SDLC procedures. Identify recurring deployment issues and improve release automation, checklists, and handoff practices. Measures of Success Improved on-time release rate and fewer failed promotions. Complete and traceable release evidence for governed deployments. Reduced release-related production incidents and avoidable rework. Reduced developer and manager time spent coordinating routine promotions. Faster, clearer response to deployment failures and production issues. Consistent adoption of release checklists, records, and environment-promotion standards. Ideal Candidate Profile The ideal candidate combines operational discipline with enough Fabric and analytics knowledge to understand dependencies across data engineering, semantic models, Power BI, testing, and production support. Success is measured by reliable, controlled releases and reduced operational friction-not by the number of deployments performed.