Software Engineering Team Manager

Own planning, prioritization, and delivery commitments for your team, and the operating rhythm that makes progress visible without a status meeting

Allocate people to work, manage escalations, and resolve blockers quickly and transparently

Recommend and land process improvements rather than escalating the same friction twice

Hire against our bar for AI-fluent engineering, and hold it when the pipeline is thin

Treat every exposed product surface as one that will carry agentic capability, and apply the same obligations whether or not the team's name says AI

Require an evaluation set with a stated pass bar, wired into the release gate, before model-backed behavior reaches a student

Ensure guardrails, human-in-the-loop fallback, escalation, and disclosure are designed into the interaction rather than added after a complaint

Monitor reliability, hallucination, and model performance against stated thresholds, and treat drift as a defect rather than as news

Hold specification-first practice: contract-level tests before the implementation, negative tests as a first-class obligation, and evals wherever behavior is model-backed

Ensure the automated merge gates run and are not routinely overridden, and treat a suite that never fails as a defect rather than a result

Drive the team's contribution to the governed shared library, so skills, MCP servers, evaluation suites, and golden datasets leave the team rather than staying in it

Coach capability deliberately, so the team's ceiling rises rather than its hours

Run performance and development conversations on a rhythm, not only at review time

Build succession inside the team, so no specialization sits with exactly one person

Lead the overall engineering delivery for Data Foundations, including roadmap execution, platform readiness, migration sequencing, cutover planning, dependency management, and coordination across source systems, downstream consumers, and partner teams

Set expectations for data contracts, quality gates, lineage, observability, access controls, operational readiness, and support ownership across the data domains the team manages

Partner closely with Product, Data Engineering, Data Analytics, Solution Architecture, Security, Compliance, our DevOps and SRE platform team, and vendor teams to align delivery priorities, resolve dependencies, and protect continuity during modernization

Collaboration

Information Security

Innovation

Problem Solving

Technical Acumen

Large Language Models (LLMs)

Generative AI

Evaluation of AI-Generated Content Quality

Performance Management

Code Review

Overseeing delivery of AI-enabled capabilities and holding the release gate

Measuring and reporting team adoption of AI tooling

Building reusable engineering tooling and evaluation suites that other teams adopt

Data foundation, integration, and migration oversight

Reliability, observability, and test discipline

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Job: Engineering

Job Family: TECHNOLOGY

Organization: Virtual Learning