Staff Software Engineer, Data Platform
Staff Engineer · Staff · Full Time · Remote
Opens Airwallex's application page
Role
What you'll do.
As a Staff Software Engineer on the Data Platform team at Airwallex, you will architect and lead the strategy for the company's foundational data and AI infrastructure, overseeing the entire data lifecycle from pipeline management to ML model governance. This high-impact role requires deep expertise in distributed systems, data platforms, and team leadership to drive the evolution of Airwallex's data ecosystem toward fully AI agent-ready infrastructure serving 250,000+ global businesses. You'll mentor a high-performing engineering team while hands-on architecting scalable solutions using Databricks, Spark, Kafka, and modern cloud platforms.
Responsibilities
- Technical Architecture Leadership: Oversee the strategy, architecture, and operation of Airwallex's data and AI platforms. Define technical direction for the data ecosystem including ETL frameworks, metrics stores, infrastructure management, and data security. Guide the team in designing scalable multi-geographical data pipelines while balancing execution velocity with rigorous engineering standards.
- Cross-Functional Problem Solving: Identify and resolve company-wide challenges using cutting-edge data platform solutions. Collaborate with stakeholders across analytics, finance, payments, and AI/ML teams to understand data needs and influence data-driven decision-making processes. Advocate for best practices in data architecture and foster a culture of innovation across the organization.
- Hands-On Technical Contribution: Provide visionary technical direction while actively participating in complex problem-solving. Contribute to the design and development of platform modules, high-performance services, and client libraries. Lead by example in implementing modern data processing patterns for lakehouse architectures using Databricks, BigQuery, Spark, and cloud-native technologies.
- Team Leadership and Mentorship: Build and mentor a high-performing data platform engineering team in a fast-paced environment. Nurture both technical and professional development of engineers, data scientists, and peers. Promote a culture of craftsmanship, ownership, and continuous learning while establishing mentorship practices that drive career growth.
- AI/ML Infrastructure Advancement: Drive the evolution of the data platform toward fully AI agent-ready infrastructure by 2026. Oversee the implementation of retrieval-augmented generation (RAG), model control protocols, and real-time performance capabilities. Enable customers to engage directly with the data platform through natural language querying and analytics while maintaining robust governance and safety standards.
Qualifications
What we look for.
Technical
Data Platform Architecture
8+ years of hands-on experience designing, building, and operating scalable data platforms. Expertise in managing complete data lifecycles including ingestion, transformation, storage, and serving. Proven track record building reliable multi-geographical data pipelines at enterprise scale with strong understanding of distributed systems design patterns.
Big Data Technologies
Deep proficiency with modern data stack tools including Databricks, Apache Spark, Apache Kafka, and orchestration frameworks such as Airflow and DBT. Experience with cloud platforms (GCP, AWS, Azure), data warehousing solutions (BigQuery, Redshift), and containerization (Kubernetes). Hands-on design experience with lakehouse architectures and modern data processing patterns.
Infrastructure and Deployment
Expert-level knowledge of building, deploying, and maintaining reliable infrastructure across multiple geographical regions. Experience with CI/CD pipelines, infrastructure-as-code, monitoring and observability systems. Strong understanding of data security, compliance requirements, and governance frameworks for sensitive financial data.
AI/ML and Advanced Analytics
Demonstrated expertise in machine learning platforms, model serving infrastructure, and MLOps practices. Understanding of retrieval-augmented generation (RAG) systems, feature stores, and real-time model inference. Experience building tools and services that empower data scientists and analysts to develop, deploy, and monitor ML models at scale.
Systems Design and Performance Optimization
Proven ability to design high-performance systems handling massive data volumes and concurrent queries. Deep understanding of query optimization, indexing strategies, and cost optimization in cloud environments. Demonstrated capability to balance engineering rigor, scalability, and operational excellence while maintaining system reliability.
Education
Bachelor's Degree in Computer Science or Related Field
Formal education in Computer Science, Computer Engineering, Mathematics, Physics, or equivalent quantitative discipline. This provides foundational knowledge in algorithms, data structures, and distributed systems theory necessary for platform architecture.
Advanced Studies (Preferred)
Master's degree or higher in Computer Science, Data Science, or related field, or equivalent professional experience demonstrating deep technical mastery in data platform engineering, distributed systems, or large-scale infrastructure development.
Experience
Data Platform Leadership
Minimum 8 years in data platform engineering or equivalent combination of work experience and academic exposure in quantitative fields. Proven experience leading company-wide initiatives across multiple teams or directly influencing technical roadmap planning. Track record of transitioning from individual contributor to technical leader managing high-performing engineering teams.
Strategic Technical Influence
Demonstrated ability to influence architecture decisions across the organization and drive adoption of data-driven practices. Experience providing technical leadership on significant projects spanning ETL frameworks, metrics infrastructure, data warehouse design, and data governance. Success in balancing execution velocity with thorough research, statistical rigor, and scalable system design.
Cross-Functional Collaboration
Proven effectiveness collaborating with diverse stakeholders including product, business, security, and infrastructure teams to drive tangible business outcomes. Experience managing technical debt, prioritizing platform improvements, and articulating technical complexity to both engineering and business audiences. Strong track record of building trust and alignment across organizational boundaries.
Mentorship and Team Development
Track record of mentoring and investing in the professional development of data scientists, engineers, and peer leaders. Experience building high-performing teams, establishing engineering standards, and fostering a culture of technical excellence and innovation. Demonstrated ability to develop future technical leaders.
Skills
Required
Distributed Systems Architecture
Expert understanding of distributed computing principles, consensus algorithms, fault tolerance, and eventual consistency patterns. Ability to design systems that handle scaling challenges across multiple data centers and geographic regions with minimal latency.
Apache Spark and Databricks
Advanced proficiency in Apache Spark for large-scale data processing, including RDD optimization, DataFrame APIs, and performance tuning. Deep hands-on experience with Databricks platform including Delta Lake, MLflow, and workspace management for enterprise deployments.
Stream Processing and Kafka
Expert knowledge of Apache Kafka for building reliable, fault-tolerant data streaming architectures. Experience designing event-driven systems, managing topic schemas, implementing exactly-once semantics, and operating Kafka clusters at scale.
SQL and Query Optimization
Expert-level SQL writing and optimization skills. Deep understanding of query execution plans, indexing strategies, cardinality estimation, and cost-based optimization. Ability to architect efficient data models and schemas for analytical and transactional workloads.
Cloud Platform Services
Hands-on expertise with GCP services (BigQuery, Dataproc, Dataflow, Cloud Storage, Pub/Sub) or equivalent AWS/Azure services. Experience designing cloud-native architectures, optimizing cloud costs, and implementing security best practices in public cloud environments.
Data Pipeline Orchestration
Advanced proficiency with workflow orchestration tools such as Apache Airflow, dbt, or Prefect. Experience designing complex DAGs, managing data dependencies, implementing monitoring and alerting, and handling failure recovery and data quality validation.
Python and Backend Development
Fluent in Python for data engineering and backend services development. Experience writing production-grade code with proper testing, error handling, and performance optimization. Ability to design and implement data transformation logic and platform services.
Team Leadership and Mentorship
Demonstrated ability to lead technical teams, conduct code reviews, establish engineering standards, and mentor junior engineers. Experience making architectural decisions collaboratively, communicating technical direction clearly, and fostering a culture of ownership and continuous improvement.
Preferred
Machine Learning Platforms and MLOps
Nice to haveExperience building MLOps infrastructure, feature stores, model registries, and serving platforms. Knowledge of ML model lifecycle management, experiment tracking, and A/B testing frameworks. Familiarity with tools like MLflow, Kubeflow, or Vertex AI.
Retrieval-Augmented Generation (RAG) Systems
Nice to haveHands-on experience designing and deploying RAG systems for enterprise applications. Knowledge of embedding models, vector databases, and prompt engineering frameworks. Understanding of how to integrate LLMs with structured data systems.
Data Governance and Security
Nice to haveExperience implementing data governance frameworks, data cataloging systems, and access control mechanisms. Knowledge of compliance requirements (GDPR, CCPA, SOX) and designing secure data platforms. Experience with data masking, encryption, and audit logging.
Kubernetes and Container Orchestration
Nice to haveAdvanced Kubernetes expertise for managing containerized data platform services. Experience with StatefulSets, operators, networking, and persistent volumes. Knowledge of Kubernetes security, resource management, and multi-cluster architectures.
Proof of Concept Methodology
Nice to haveAbility to rapidly evaluate emerging technologies and conduct technical proofs of concept to validate architectural decisions. Demonstrated success in building prototypes quickly, measuring key performance indicators, and making data-driven technology recommendations.
Financial Services or FinTech Domain Knowledge
Nice to haveExperience building data platforms in financial services, payments, or related high-compliance domains. Understanding of financial data models, real-time settlement requirements, transaction processing, and regulatory reporting needs. Knowledge of specific challenges in global financial infrastructure.
Cost Optimization and Financial Management
Nice to haveExperience optimizing cloud infrastructure and platform costs. Ability to balance performance requirements with cost efficiency, implement resource management strategies, and optimize query costs in cloud data warehouses.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 200,000 – 260,000
Equity·Stock options
Benefits
Competitive Equity Compensation
Significant stock option grants reflecting Airwallex's $11 billion valuation and growth trajectory. Equity provides long-term wealth creation opportunity as the company scales its global fintech platform.
Comprehensive Health Coverage
Premium medical, dental, and vision insurance coverage with competitive employer contribution. Coverage typically includes mental health services and wellness programs supporting holistic employee wellbeing.
Professional Development Investment
Learning budget and access to professional development resources, conference attendance, and skills development programs. Support for certifications and technical training aligned with career growth in data engineering.
Flexible Work Arrangements
Flexibility in work location and schedule supporting work-life balance. Many staff-level engineers at Airwallex work with hybrid or flexible arrangements as they manage strategic responsibilities.
Retirement Planning
401(k) retirement plan with employer matching contributions (US-based). International locations include equivalent retirement savings programs compliant with local regulations.
Generous Time Off
Competitive paid time off, sick leave, and parental leave policies. Airwallex supports employee wellbeing through flexible time-off practices enabling recovery and personal priorities.
Global Mobility Support
As a company with 27 offices globally, support for mobility including relocation assistance if joining from another location. Potential for international project opportunities and cross-office collaboration.
Innovation and Impact
Opportunity to work on transformative data and AI infrastructure serving 250,000+ global businesses. Direct impact on company strategy and ability to influence the future of fintech payments infrastructure.
Full posting
Original listing.
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 250,000 businesses worldwide – including Brex, 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 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, 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.
Attributes We Value
We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you’re motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.
You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end-to-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.
About the team
The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. This includes managing our data infrastructure (Databricks, Spark, Kafka, etc.), the technology to serve that data to our users (RAG, MCP, etc.), and the platform to host and govern these AI/ML models.
In 2026, our team’s overarching mission is to evolve our full data ecosystem—encompassing both platform and models—into a fully AI agent-ready infrastructure; we will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making.
What you'll do
As a high level architect (staff engineer), you will oversee the strategy, architecture, development, and operation of Airwallex's data and AI platforms. You will play a pivotal role in influencing data-driven and AI-powered decision-making processes. A key aspect of your role will also include building a high-performing team that excels in a fast-paced environment. Your leadership will be critical in mentoring the team, promoting a culture of innovation, and driving technical excellence.
This role is based in Seattle.
Responsibilities：
Spearheaded the identification and resolution of Airwallex-wide challenges using cutting-edge data platform solutions.
Provide visionary technical direction, fostering a community within Airwallex's data realm, and actively leading in solving complex problems hands-on.
Advocate for best practices across the data platform, instilling a culture of craftsmanship and innovation.
Mentor the data platform team, nurturing both technical and professional development.
Who you are
We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.
Minimum Requirements:
A minimum of 8 years of experience in Data Platform or an equivalent combination of work and academic exposure in a quantitative field.
Proven experience leading company-wide initiatives across multiple teams or influencing tech roadmap planning.
Effective collaboration with diverse teams and stakeholders to drive tangible business outcomes.
Demonstrated ability to balance execution and velocity with in-depth research, statistical understanding, and scalable design.
Track record of mentoring and investing in the development of scientists, engineers, and peers.
Experience providing technical leadership on significant projects, covering ETL frameworks, metrics stores, infrastructure management, and data security.
Proficiency in building, deploying, and maintaining reliable multi-geographical data pipelines at scale.
Familiarity with workflow or orchestration frameworks such as Airflow, DBT, etc.
Preferred Qualifications:
Hands-on design experience in crafting data processing patterns for a modern Lakehouse architecture.
Contribute to the design and development of standard framework modules, high-performance services, and client libraries for big data using tools like GCP, Databricks, BigQuery, DataProc, Kafka, Kubernetes, Spark, DataFlow, Google Cloud Storage, and Airflow.
Excellent written and verbal communication skills tailored for diverse audiences (leadership, users, company-wide).
Ability to rapidly evaluate various technologies and conduct proof of concepts to drive architecture design.
Experience thriving in a complex environment
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Applicant Safety Policy: Fraud and Third-Party Recruiters
To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page.
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.
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.
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