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 WebForms and 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 legacy and the new.
The platform is becoming agentic across every exposed product area, not only in the teams whose 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 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
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 experience this role is assessed 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