Senior Engineering Manager (TechOps), TradeNet

GovTech; Singapore; On-site; SGD 250,000 - 360,000; Full time

Job summary

GovTech in Singapore seeks a senior TechOps leader to build and scale a multi-disciplinary team responsible for platforms, tooling, and operations across DevOps, QA, databases, and security. You will define team structure, career ladders, and drive delivery milestones with a strong focus on automation and governance.

You will own critical platforms, enable self-service, and oversee production support, incident management, and security accreditation activities in a government context that

Qualifications

• 12+ years in technology with 6+ years in engineering leadership.
• Experience standing up a support organisation with defined Tier 1/2/3 structures and escalations.
• Proven track record in cloud cost optimisation at scale with measurable financial outcomes.

Responsibilities

• Build, hire, and grow a multi-disciplinary TechOps team (10+ engineers, growing).
• Define team structures, career ladders, and progression frameworks; clarify interfaces with product squads and Architecture.
• Own OKRs and delivery milestones for TechOps; report risks and capacity constraints to programme leadership.
• Set standards: automate by default, measure what matters, fix what's broken.
• Own engineering platforms: CI/CD, IaC, quality gates, security scanning, observability, and database services.
• Deliver governed self-service so squads deploy independently within guardrails.
• Own release management metrics: deployment frequency, lead time, change failure rate, MTTR.
• Establish production support operations and incident management frameworks.
• Drive compliance readiness: audit evidence, security accreditation (CSA/CSG/IM8).
• Drive AI-assisted engineering workflows: standardized tooling, AI-augmented reviews, test generation.
• Hold engineering output to high quality, whether human- or AI-written.

Skills

Engineering leadership

FinOps & cloud cost optimization

Cloud infrastructure

Data platforms

AI-assisted engineering

Vendor & alliance management

Compliance in government environments

Education

Bachelor's degree in CS or related field

Tools

Terraform

Kubernetes

CI/CD pipelines

Observability tooling

Job description

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