Tech Lead
Software EngineeringPosted 10 Jul 2026
Advertised as “Tech Lead, Finance & Supply Chain Engineering”
Posted 10 Jul 2026
Advertised as “Tech Lead, Finance & Supply Chain Engineering”
The original posting with the fluff stripped out
| Base salary | US$230,000 – US$385,000 / yr |
|---|
The Finance & Supply Chain Engineering organization includes two complementary teams. Software Engineering builds internal full-stack applications, durable agentic workflows, plugins, MCPs, and measurable AI-enabled engineering practices. Data Engineering builds trusted analytics data assets for Finance and Supply Chain. The teams have distinct charters, with important shared dependencies and broad cross team partnerships across Engineering, Applications, Finance, and Supply Chain.
We are looking for a hands-on senior technical leader who will report alongside the Software Engineering and Data Engineering managers. This is an individual-contributor role with no immediate people-management responsibility. The Tech Lead will raise the technical bar across both teams, participate in important cross-team or high-risk design decisions, and directly own and ship high-impact work. The role should improve team judgment and autonomy rather than act as a floating architect or universal approval gate.
In this role, you will:
Partner with the Software Engineering and Data Engineering managers as a peer technical leader; managers retain accountability for people, staffing, priorities, performance, and delivery commitments.
Directly own the architecture, implementation, launch, and operation of one or more high-impact initiatives, remaining accountable for real outcomes rather than advisory output alone.
Guide important design decisions that are cross-team, difficult to reverse, or material to security, financial controls, reliability, data quality, or long-term cost of ownership.
Establish pragmatic engineering standards across architecture, APIs and data contracts, testing, security, reliability, observability, lineage, data quality, and operational ownership.
Advance engineering standards for building with AI, including agentic workflows, evaluation, telemetry, adoption, and outcome measurement.
Work across backend, full-stack, data, and big-data systems, making sound boundary decisions and bringing the right domain experts into the design.
Partner deeply with Engineering, Applications, Product, Finance, Supply Chain, Security, and other stakeholders to translate ambiguous needs into durable technical systems.
Raise the technical capability of both teams through design reviews, code reviews, mentoring, written guidance, and reusable reference implementations.
Protect team autonomy: focus involvement on consequential decisions, clarify principles and tradeoffs, and avoid becoming a required approver for routine or reversible work.
Have significant professional engineering experience with sustained staff-level or equivalent impact as an individual contributor; prior people management is not required, but a plus.
Can reason across a full-stack application—from user experience and client architecture through backend services, integrations, authorization, and durable state.
Have strong data-engineering judgment across analytical modeling, batch or streaming pipelines, orchestration, lineage, quality, scale, and downstream data contracts.
Have personally designed, built, shipped, and operated consequential systems, and can go from architecture to implementation details without relying on positional authority.
Have led important technical decisions and cross-team initiatives through influence, clear reasoning, and trusted partnership.
Care deeply about correctness, security, data quality, auditability, operational excellence, and measurable outcomes.
Communicate clearly with engineers, managers, and non-technical partners; lead with context and principles rather than control.
Experience building internal products, developer platforms, workflow systems, or analytics platforms used across multiple teams.
Experience with LLM applications, durable agents, AI evaluations, plugins, MCPs, or measurement of AI-enabled engineering outcomes.
Experience in Finance, Supply Chain, accounting, procurement, ERP, or another domain where controls, reconciliation, and traceability matter.
About OpenAI
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