Senior Engineering Manager (TechOps), TradeNet
AI Summary
Builds and leads a multi-disciplinary TechOps team of 10+ engineers across DevOps, QA, database engineering, security ops, production support, and observability. Owns platform delivery, CI/CD, IaC, quality gates, compliance readiness, and AI-assisted engineering standards for a government trade platform.
About this role
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
Skills
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