Software Engineering Team Manager

Pearson Virtual Schools serves K-12 students and their teachers on Connexus, a large-scale student, curriculum, and assessment data platform. The Data Foundations project establishes the trusted data platform, ownership model, migration path, and engineering operating model needed to make this data reliable, secure, accessible, and usable for analytics, operational reporting, interoperability, and future AI-enabled product capabilities. This role is accountable for leading the engineering execution required to modernize that foundation while protecting continuity for current platform consumers.

We are modernizing a platform that today runs primarily on legacy .NET Frameworks: IIS-based ASP.NET WebFormsand MVC, an AngularJS and jQuery front end, and SQL Server. The future is design-system-based React and TypeScript single-page applications calling .NET REST services that operate in containerized modern .NET, and integrate with an API-first, event-driven model on AWS with externalized authorization. Your teams will work on both sides of this technology, the legacyand the new.

The platform is becoming agentic across every exposed product area, not only in the teamswhose names say AI. That changes what an engineering manager is accountable for. Wherever your team's surface reaches a student, a family, a teacher, or school staff, you own the guardrails, the human-in-the-loop fallback, the disclosure, and the evidence that the behavior was evaluated before release. That obligation does not vary by team.

You are expected to be AI-native in how you manage. That means you can review work produced by engineers operating at our bar for AI-fluent engineering, understand the role and limits of evaluation sets, and treat quality as a release gate rather than a late-stage phase.

Key Responsibilities

Team Delivery and Operating Rhythm

• 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

Holding the Shape of the Team

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

Agentic Capability and Duty of Care

• Treat every exposed product surface as one that will carry agentic capability, and apply the same obligations whether or notthe 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

Quality, Adoption, and Shared Tooling

• 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

People and Capability

• 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

Domain Depth for This Posting

• 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

Preferred Qualifications

• 4+ years managing engineers who ship to production, including hiring, performance, and development

• A working engineering background, enough to review a technical decision and to be credible with engineers operating at our bar for AI-fluent engineering

• Experience delivering a model-backed or agentic capability to real users, including how it was evaluated before release and monitored afterward

• Practiced at holding a quality gate under delivery pressure, and able to describe a time you held one and a time you should have

• Experience managing across a legacy and a target platform at the same time, where the team cannot stop shipping on either

• Comfortable managing a distributed team across time zones, with the written discipline that requires

• Experience in regulated or privacy-sensitive environments; K-12 and FERPA exposure an advantage

• Relevant degree or equivalent practical experience

Skills and Experience

The skills and experiencethis role isassessed against.

Core skills

• Adaptability

• Artificial Intelligence

• Learning to Learn

Engineering fundamentals

• Collaboration

• Information Security

• Innovation

• Problem Solving

• Technical Acumen

Role-specific skills

• API Management

• Cloud Computing

• Data Security

• DevOps

• Ethical Responsibility

• Resilience Planning

• Software Design

Emerging skills

• AI Agents

• Automated Artificial Intelligence (AutoAI)

Specialist depth for this role

• 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