Senior Manager, Growth Technology Lead

Own and evolve the Marketing technology roadmap — defining the strategy, prioritisation, and sequencing of investments in tooling, data, and AI capability across the entire Marketing function.

Lead and grow a team - creating a culture of ownership, experimentation, and high performance.

Drive AI adoption across Marketing and GTM — identifying, building, and scaling use-cases that meaningfully improve funnel efficiency, lead quality, content output, and campaign performance, with a preference for building rather than buying where it creates a durable advantage.

Take ownership of Marketing’s data infrastructure — partnering with Data & Analytics to build in-house data capabilities including a Marketing-owned Postgres lakehouse, enabling the team to operate with greater autonomy and speed in accessing, modelling, and acting on marketing data.

Own Marketing’s internal tooling platform — maintaining and extending tools used daily by 160+ Go-to-Market users to improve funnel visibility, lead-to-opportunity conversion, and decision-making speed.

Represent Marketing Technology at a senior leadership level — bringing forward big initiatives, contributing to the Marketing Leadership Team (MLT) planning process, and building stakeholder confidence in the team’s roadmap and commercial impact.

Establish and maintain project delivery rigour across the team — implementing the right processes and tooling (e.g. Jira) to track workload, manage cross-functional dependencies, and ensure external stakeholders are engaged early enough to avoid bottlenecks.

Growth Strategy & GTM Systems

Own the growth engine for New Business and Expansion pipeline — from lifecycle programs and trigger-based outreach to self-serve and sales-assisted motions.

Translate GTM strategy (from Sales, RevOps, and Marketing leadership) into scalable, systemized programs rather than one-off campaigns.

Identify structural gaps in the funnel — lead surfacing, conversion, dunning, retention, expansion triggers — and design solutions that fix them at the system level, not just the symptom.

Marketing & GTM Engineering Leadership

Lead a team of spanning growth marketers and marketing/GTM engineers, setting the balance between strategic campaign work and technical build.

Direct the team's use of the marketing technology stack (e.g. Salesforce, HubSpot, Customer.io, Segment, Workato) to build automated, repeatable growth workflows — without owning the underlying platform architecture (that's GTM Engineering's remit).

Build a team culture of experimentation: rapid test-and-learn cycles, clear success criteria, and fast kill/scale decisions.

Experimentation & Optimization

Build and prioritize an experimentation roadmap across acquisition, lifecycle, and expansion — connected directly to pipeline and revenue impact, not vanity metrics.

Own reporting and insight-sharing on what's working, what isn't, and why — partnering with Data & Analytics to keep measurement rigorous.

Continuously evaluate and pilot AI-powered tools and workflows to increase team output and shorten the time from idea to shipped program.

Cross Functional Partnership

Partner closely with Sales leadership and RevOps to ensure growth programs are directly tied to New Business and Expansion targets.

Work with Data & Analytics to define and track KPIs across the funnel (MQL/SQL volume, conversion rates, pipeline contribution, retention/expansion signals).

Represent Growth/Marketing's voice in cross-functional planning, and act as the connective layer between Marketing strategy and GTM Engineering's technical roadmap.

AI Skills

Actively uses AI tools daily to accelerate and improve work — from automating workflows and generating content, to analysing data and prototyping solutions — and creates the conditions for their team to do the same.

Clear point of view on where AI creates durable competitive advantage vs. where off-the-shelf solutions are sufficient — with a track record of building AI-powered capabilities that ship and create measurable impact, not just proof-of-concept work.

Able to evaluate AI-generated output critically — understanding the limitations of models and tooling, and applying appropriate quality standards before shipping AI-powered systems to internal or external users.