Tooling / Platform Engineer – Harness

Job Description 1. Software Delivery Platform Ownership – Harness - Own the Harness platform as the client’s enterprise delivery platform, including architecture, configuration standards, RBAC, governance model, module adoption roadmap, and vendor relationship. - CI (Continuous Integration): Establish reusable pipeline templates, shared libraries, build caching, parallelization standards, and CI patterns to maintain build times within target as the organization scales. - CD (Continuous Delivery): Define deployment strategies, including progressive delivery, canary, and blue-green deployments; GitOps workflows; environment and artifact promotion paths; approval gates; and change-control evidence for regulated payment workloads. - STO (Security Testing Orchestration): Integrate SAST, DAST, SCA, container, and secrets scanning across pipelines. Collaborate with AppSec to define severity thresholds, blocking versus advisory policies, exemption workflows, and remediation SLAs. - SEI (Software Engineering Insights): Define engineering metrics, including DORA, flow, lead time, and review latency. Establish appropriate guardrails to ensure metrics identify system-level constraints rather than evaluate individual engineers. - CCM (Cloud Cost Management): Implement cloud cost visibility, budgets, anomaly detection, non-production autostopping, and showback or chargeback models. Connect architecture decisions to unit economics and cost optimization. - Establish policy-as-code standards to ensure pipeline governance is versioned, reviewable, and auditable rather than manually configured through a UI. 2. SDLC Toolchain Ownership - Own the software delivery toolchain, including source control, issue tracking, artifact management, static analysis, and test infrastructure. - Manage integration architecture, entitlements, license management, upgrade and migration planning, and consolidation of redundant tools. - Define and maintain golden paths: documented, supported, and standardized workflows that enable teams to move from code commit to production, with deviations treated as explicit decisions. 3. AI in the Engineering Workflow - Establish guidelines for using AI tools in software design, code generation, testing, and documentation. - Define controls for safe AI adoption, including evaluation criteria, human review requirements, context and data-handling standards, and governance for AI-generated code entering production. Location: Atlanta, GA (Day 1 Onsite) Duration: 6–12+ Months Contract Engagement Type: Contract Work Arrangement: Onsite from Day 1