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