Tech Enablement Designer – Change & Adoption

Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making it the financial technology platform of choice.

For its teams, Adyen creates an environment with opportunities for people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. The company is motivated by individuals who tackle unique technical challenges at scale and solve them as a team. Together, they deliver innovative and ethical solutions that help businesses achieve their ambitions faster.

Tech Enablement Designer – Change & Adoption

Tech Academy is a strategic partner to Adyen’s global Tech organization – Engineering, Product, and Product experience – making teams faster and more effective by giving them the skills and know-how they need to ship products and solve real customer problems. Learning is treated as part of the workflow, not as a one-off event, and success is measured by improvements in delivery speed, quality, and clarity.

Adyen is hiring a hands-on Tech Enablement Designer who will sit close to Tech teams, understand how work really happens, and help them adopt the enablement, tools, and practices.

This is a builder role focused on change and adoption - you’ll make it easier for engineers, PMs, and PX to adopt and improve ways of working, learn new skills and move through the change effectively.

In this role, you will:

• Focus on helping Tech teams move from “this is the new way we want to work” to “this is how we do it at Adyen”. This is not a training-delivery or “defining how Tech should work” role; it is focused on change, enablement, and adoption.

• Partner with Tech enablement and Tech teams to understand how work is currently done; where people get stuck, and what makes adoption harder than it needs to be;
• Turn enablement and expectations into clear, practical support for day-to-day use, launching different types of interventions;
• Plan and run change and adoption activities tailored to Tech (for example, short walkthroughs, focused sessions with leads, async explainers, or guided “first-time” use) rather than broad, generic training;
• Set up and support peer-to-peer formats where teams share how they’ve adopted a new enablement or tool in practice, so practical patterns spread across Tech without heavy coordination;
• Run small adoption experiments (for example, different ways of introducing a change or supporting a new tool), observe what teams actually use, and iterate based on feedback and basic usage signals;
• Use AI as a thought partner in your own work;
• Design and support upskilling interventions with Tech enablement (for example, learning sprints, targeted skill sessions, or AI-focused practice labs) that help Tech teams build the skills needed to adopt new ways of working;
• Shape future enablement and upskilling topics based on what teams actually need and run discovery work on a constant basis making sure we are solving the right problems.

Who You Are

You are comfortable working close to Tech, translating change into something people can actually use, and using AI as a normal part of your work.

• Experience helping Tech teams (Engineering, Product, PX) adopt new tools, practices, or ways of working – for example through internal enablement, change management, coaching, or learning design close to the work;
• Experience turning “this is what we want to do” into clear, practical support: guides, templates, learning paths, FAQs, short explainers, or similar that fit into existing tools and routines;
• Hands-on experience planning and running change and adoption activities (e.g. small group sessions, async explainers, pilots, or “first-time use” support) rather than only large training events;
• Comfortable using AI as a thought partner – to explore options, analyse feedback, create and refine interventions, and generate concrete examples or use cases;
• Ability to design and support upskilling interventions (e.g. learning sprints, targeted sessions, AI-focused practice labs) that are simple, focused, and tied to real work and adoption;
• Strong written and verbal communication skills: you can explain changes in simple language, adapt the message to different Tech audiences, and handle questions and pushback;
• Used to working in an experiment-driven way: you test different approaches, watch what teams actually use, and iterate based on feedback and simple adoption signals.