Director of Engineering, Growth & Ecosystem (Relocate to Singapore)

Airwallex
London
2 months ago
Applications closed

Related Jobs

View all jobs

Director of Generative AI | Remote

Product Director - Digital Health

Marketing Machine Learning Engineer

Senior Recruiter

Data Engineering Lead - Finance and Master

Senior Data Analyst

About Airwallex

Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 100,000 businesses worldwide – including Brex, Rippling, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.

Proudly founded in Melbourne, we have a team of over 1,500 of the brightest and most innovative people in tech located across more than 20 offices across the globe. Valued at US$5.6 billion and backed by world-leading investors including Sequoia, Lone Pine, Greenoaks, DST Global, Salesforce Ventures and Mastercard, Airwallex is leading the charge in building the global payments and financial platform of the future. If you're ready to do the most ambitious work of your career, join us.

About the team:

The Engineering team at Airwallex is a diverse group of innovators, builders, and problem solvers driven by a mission to empower businesses to operate anywhere, anytime. We thrive in a collaborative and fast-paced environment where we constantly push the boundaries of what's possible in financial technology. As a team, we value technical craftsmanship, continuous learning, and a strong sense of ownership, working together to build scalable, reliable, and secure products that empower businesses of all sizes to grow without borders.

Job Overview:

As theDirector of Engineering, Growth & Ecosystem, you will drive the technical strategy and execution for user acquisition, activation, retention, and revenue growth. You will lead a team of talented engineers, working closely with product, data, marketing, and design teams to identify and execute growth opportunities across the customer lifecycle. Your primary goal will be to build scalable growth platforms, develop data-driven experimentation processes, and optimize our product features to enhance user engagement and maximize business growth.

This role is based inSingapore.

Key Responsibilities:

  • Strategic Leadership:Develop and implement a comprehensive growth engineering strategy that aligns with the company’s business goals.

  • Team Management:Lead, mentor, and grow a team of engineers focused on growth initiatives, including hiring and developing top talent.

  • Cross-Functional Collaboration:Partner with product, marketing, data science, and design teams to prioritize and execute growth experiments and initiatives.

  • Data-Driven Optimization:Utilize data analysis, A/B testing, and experimentation to identify growth opportunities, optimize conversion rates, and enhance the overall user experience.

    • Increase user sign-ups or registrations

    • Enhance user engagement and retention

    • Optimize the user experience to reduce churn

    • Drive revenue through better conversion rates

  • Platform Development:Oversee the development and maintenance of scalable systems, tools, and frameworks to support growth initiatives, including marketing automation, personalization, and customer segmentation.

  • Performance Monitoring:Establish key performance indicators (KPIs) for growth, track progress against these metrics, and report on performance to senior leadership.

  • Market Insights:Stay up-to-date with industry trends, competitor activities, and emerging technologies to ensure the company’s growth strategy remains innovative and competitive.

Qualifications:

  • Experience:10+ years of experience in software engineering, with at least 3-5 years in a growth-focused role, preferably in a leadership position.

  • Leadership:Proven track record of leading and growing high-performing engineering teams.

  • Technical Skills:Strong proficiency in modern programming languages (e.g., Python, JavaScript, Go), data analysis tools (e.g., SQL, R, Python), and experience with growth-related technologies (e.g., analytics platforms, marketing automation).

  • Growth Expertise:Deep understanding of growth tactics, A/B testing, experimentation frameworks, and data-driven decision-making processes.

  • Analytical Mindset:Strong analytical and problem-solving skills, focusing on quantitative analysis and the ability to leverage data to drive decisions.

  • Collaboration:Excellent communication and interpersonal skills, with the ability to work effectively with cross-functional teams and stakeholders.

  • Education:Bachelor’s degree in Computer Science, Engineering, or a related field; a Master’s degree is a plus.

Preferred Qualifications:

  • Experience in a fast-paced, high-growth startup environment.

  • Knowledge of machine learning, AI, or advanced analytics techniques.

  • Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and microservices architecture.

Equal opportunity

Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.

Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.