Deputy Director, Engineering Management (TechOps), TradeNet

GovTechSingaporeJob.bozveřejněno 31. 08. 2026
Nutné:AWSCloudDevOpsCI/CDAISecurityLead

What you'll do Organisation building & leadership

  • Build, hire, and grow a multi-disciplinary TechOps team (10+ engineers, growing) spanning DevOps, Quality Engineering, Database Engineering, Security Operations, Production Support, and Observability.
  • Define team structures, RACI, roles, career ladders, and progression frameworks. Clarify interfaces between TechOps pillars, product squads, and the Architecture function.
  • Own OKRs and delivery milestones for TechOps. Report progress, risks, and capacity constraints to programme leadership before they become blockers.
  • Set the standard for how TechOps works: automate by default, measure what matters, fix what's broken.

Platform & delivery

  • Own the engineering platforms that product squads depend on: CI/CD pipelines, infrastructure-as-code, quality gates, security scanning, observability, and database services.
  • Deliver governed self-service so squads deploy independently within approved guardrails — without waiting on your teams for routine operations.
  • Own the Green Lane and make releasing uneventful: deployment frequency, lead time, change failure rate, MTTR.
  • Establish production support operations (L1/L2/L3), on-call processes, and incident management frameworks.
  • Drive compliance readiness: audit evidence, security accreditation (CSA/CSG/IM8), and go-live gates for each programme wave.

Engineering standards & AI-assisted practices

  • Set and enforce TNR-wide engineering standards across code quality, testing, review processes, and deployment safety.
  • Drive AI-assisted engineering workflows: standardised tooling, commit attribution, AI-augmented code review, test generation, and quality evaluation.
  • Hold all engineering output to the same quality bar whether it was written by a human or an AI agent.

What we are looking for

  • At least 12 years of experience in technology, with 6+ years in engineering leadership managing platform, infrastructure, or operations teams at scale.
  • Demonstrated experience building and leading a support organisation — not just overseeing one, but standing it up: hiring, defining tiers (L1/L2/L3), setting quality standards, managing escalations, and coaching engineers. You understand production support from the inside.
  • Proven track record in FinOps and cloud cost optimisation at scale — driving organisational change to embed cost-efficiency into engineering culture, not just reporting on spend. Experience delivering material financial outcomes (not just dashboards).
  • Experience with data platforms and cloud infrastructure — data lake architecture, large-scale cloud migrations, or database engineering. Hands-on enough to make sound technical tradeoffs on data strategy and cloud architecture decisions.
  • Track record managing multiple engineering disciplines simultaneously

(infrastructure + support + data, or similar). Single-function management experience is not sufficient.

  • Experience executing large-scale cloud migrations — moving significant workloads to production cloud environments under time pressure, not just writing migration plans.
  • Strong vendor and alliance management — experience as executive liaison with major cloud providers, technology vendors and system integrators, including partnership governance.
  • Experience in compliance-heavy environments (government, financial services, critical infrastructure) where audit evidence, security accreditation, and change governance are non-negotiable.
  • Comfortable working alongside teams that use AI development tools daily. You don't need to write prompts yourself, but you need to understand AI-assisted engineering well enough to set standards and judge output quality.

Good to have

  • Experience with the Singapore government technology ecosystem (GovTech, SGTS, IM8/CSA frameworks).
  • Background in trade, logistics, or regulatory platform engineering.
  • Experience with AWS at enterprise scale.
  • Familiarity with AI-assisted development workflows and agentic engineering practices.
  • Experience with large-scale legacy modernisation programmes, and/or regulated cutover