- The technical plan and delivery for several technology tracks, such as
consolidations, migrations, and other large-scale initiatives, from target design through launch.
- Migrations of large volumes of content and data, such as assessment items or
media assets, with verification that nothing is lost or broken for downstream products.
- Shared capabilities that should be built once and used everywhere, like media
handling, search, rendering, or AI content services, exposed through clean APIs.
- An honest, costed view of the technical debt in your systems and what to fix
first.
- Escalated incidents on your systems: you are the technical point of contact
when one has business impact, and you stay on it through root cause and the fix that prevents a repeat.
- Security and stability for the applications you own. That covers runtime and
framework upgrades, vulnerability and penetration test remediation, secure data handling, and disaster recovery plans that are documented, exercised, and audit ready.
- Shared standards across teams for design patterns, tooling, reliability,
observability, performance, and evaluation, including how AI-powered workflows are measured, tested, and kept safe.
- The long-term technical roadmap for your area, and the subject-matter
expertise that comes with it.
How you will work
This is a hands-on engineering role, not an architecture role. You will not be embedded in a single team; you will work across the platform on initiatives and technical problems that matter most to leadership. You own the designs, the code on the critical path, and the engineering calls that decide whether an initiative succeeds. You should enjoy defining the problem as much as solving it.
- Write production code where the risk is highest and review the hardest
changes from other engineers.
- Unblock hard technical problems that span systems, teams, or domains.
- Work with engineering managers, product managers, architects, and quality
engineering. When long-term initiatives and current delivery compete for the same people or code, make the tradeoff visible and help decide it.
- Work directly with the people who use the platform across Pearson's lines of
business, and separate the foundational problems from the incremental ones before the team commits.
- Spot opportunities that cut across team and product boundaries, bring
stakeholders to a shared direction, and carry the work from exploration through launch and iteration.
- Make high-consequence technical decisions under ambiguity, balancing speed
and learning against scalability, simplicity, and long-term maintainability. Hold views grounded in evidence, and change them when the evidence changes.
- Evaluate frameworks, tools, and vendors, and pilot practices like contract
testing, automated regression, and zero-downtime migration before teams adopt them.
- Run technical feasibility reviews for new AI capabilities, and work with
internal teams and external partners to assess and co-develop them.
- Use AI coding tools daily, particularly for large upgrades and refactors, and
help teams adopt them with sound review habits.
- Act as a technical multiplier: review critical designs, mentor engineers, and
improve engineering practices. Success includes the senior engineers around you becoming owners of these systems too.