Tooling Engineer - Harness

Tooling Engineer - Harness

Must Have Technical/Functional Skills

Software delivery platform ownership - Harness

Own the Harness platform as enterprise delivery platform: architecture, configuration standards, RBAC and governance model, module adoption roadmap, and the vendor relationship.

CI - Establish reusable pipeline templates and shared libraries, build caching and parallelization standards, and CI patterns that keep build times inside[target] as the org scales.

CD - Define deployment strategies (progressive, canary, blue-green), GitOps workflow, environment and artifact promotion paths, and the approval gates and change-control evidence required for regulated payment workloads.

STO - Orchestrate SAST, DAST, SCA, container, and secrets scanning across pipelines. With AppSec, set severity thresholds, decide what blocks versus advises, and own the exemption workflow and remediation SLAs. Vulnerability findings without a functioning triage path are noise, not security.

SEI - Define the engineering metrics the organization runs on - DORA, flow, lead time, review latency - and, equally, the guardrails on their use. This data is for identifying system-level constraints, not for evaluating individual engineers; own that boundary explicitly and defend it.

CCM - Deliver cloud cost visibility, budgets, anomaly detection, non-production autostopping, and showback or chargeback to product lines. Connect architecture decisions to unit economics so cost is a design input rather than a quarterly surprise.

Set policy-as-code standards so pipeline governance is versioned, reviewable, and auditable rather than configured by hand in a UI.

SDLC toolchain

Own the toolchain around delivery - source control, issue tracking, artifact management, static analysis, test infrastructure - including integration architecture, entitlement and license management, upgrade and migration planning, and consolidation of redundant tools.

Define golden paths: the documented, supported, default way a team goes from commit to production, with deviation as an explicit decision rather than drift.

AI in the engineering workflow

Establish how AI tooling is used in design, code generation, testing, and documentation - and the controls that make it safe: evaluation criteria, human review requirements, context and data-handling standards, and governance for generated code entering production.

Required qualifications

[10]+ years in software engineering, solution architecture, or platform engineering, including senior architecture leadership on distributed, high-availability systems.

Experience in payments, fintech, merchant services, or a comparably regulated transaction-processing domain.

Ownership of an enterprise CI/CD platform - not just use of one. Pipeline-as-code, templating and reuse at scale, RBAC design, secrets management, and multi-team governance. Harness, GitLab, Azure DevOps, GitHub Actions with Argo or Spinnaker, or Jenkins operated at scale.

Experience integrating security scanning into delivery pipelines and setting policy gates that engineering teams accept rather than route around.

Working command of engineering delivery metrics - DORA or equivalent - including their limits and the ways they get misused.

Depth in API and integration architecture: REST, asynchronous messaging, event-driven patterns, API lifecycle governance.

Cloud-native architecture and Kubernetes in production.

Track record of influencing technical direction across teams without reporting authority, and of explaining trade-offs to executives.