# Staff Data Engineer, Data and AI, Customer Data Platform **Company:** [Airwallex](https://scaleengineer.com/companies/airwallex) Staff Data Engineer role at Airwallex, a leading AI-native financial operating system, focused on building and scaling the Customer Data Platform's data foundations. You'll design and implement scalable ETL pipelines, develop sophisticated data models supporting machine learning and AI applications, and partner with globally distributed teams to deliver critical data infrastructure. Requires 7+ years of data engineering experience with deep expertise in batch and streaming technologies, SQL, data warehousing, and modern cloud platforms. **Role:** Data Engineer **Seniority:** Senior **Locations:** SG - Singapore **Salary:** 150000–220000 SGD [Apply](https://jobs.ashbyhq.com/airwallex/10c64a9c-8e6d-44fe-8af6-4cbcf86f9ff0) Canonical: https://scaleengineer.com/jobs/airwallex/staff-data-engineer-data-and-ai-customer-data-platform --- ## Responsibilities - Design and Own Scalable ETL Pipelines: Architect, build, and maintain robust ETL (Extract, Transform, Load) pipelines across batch and real-time workflows. Leverage technologies such as Databricks, Apache Spark, and DBT to process large-scale data efficiently. Own the entire lifecycle from design through production monitoring, ensuring pipelines handle high-volume transactional data from Airwallex's diverse customer base including enterprises like Qantas and SHEIN. - Build and Evolve Data Foundations: Develop reliable, scalable data infrastructure spanning data warehouses, data consumption platforms, and modern cloud data infrastructure on Google Cloud Platform. Establish data quality standards, implement monitoring and alerting frameworks, and continuously optimize data pipelines for performance and cost-efficiency. Ensure data integrity and governance across the entire data platform supporting 675,000+ businesses. - Develop Advanced Data Models and Products: Create sophisticated data models and reusable data products using star schema, snowflake, normalized, and denormalized designs. Build data foundations that support multiple use cases simultaneously: operational decision-making, business intelligence analytics, machine learning feature engineering, and AI/agentic workflows. Work cross-functionally to understand business requirements and translate them into optimized data architecture. - Apply Data Engineering and AI Best Practices: Implement industry best practices for data engineering, data quality, lineage tracking, and AI/ML integration. Apply creative and scalable solutions to solve complex problems in financial data processing, real-time event streaming, and customer data unification. Stay current with emerging technologies and methodologies in data engineering and AI applications. - Collaborate with Globally Distributed Teams: Partner with engineering, analytics, machine learning, and product teams across Airwallex's 27 offices and multiple continents. Communicate complex technical concepts clearly, document architectural decisions, and provide technical leadership on data platform initiatives. Work in the Singapore office five days per week, fostering strong collaboration and alignment with distributed team members. ## Requirements ### education - {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Bachelor's degree or higher in Computer Science, Information Systems, Mathematics, Physics, or related discipline. Strong foundation in computer science fundamentals, algorithms, and system design principles."} ### technical - {"name":"ETL Pipeline Architecture","description":"7+ years of proven experience designing and implementing scalable ETL pipelines supporting both batch processing and real-time streaming workflows at enterprise scale. Demonstrated ability to handle complex data transformations and ensure pipeline reliability in production environments."} - {"name":"Streaming Technologies","description":"Hands-on expertise with streaming platforms such as Apache Kafka or Apache Flink. Experience building real-time data pipelines, event-driven architectures, and handling low-latency data processing requirements for operational systems."} - {"name":"SQL and Database Management","description":"Advanced SQL proficiency with deep understanding of relational database management systems (RDBMS), data warehousing concepts, and query optimization. Strong background in database performance tuning, indexing strategies, and transaction management."} - {"name":"Data Modeling and Schema Design","description":"Expert-level understanding of data schema design patterns including star schema, snowflake schema, normalized, and denormalized models. Ability to evaluate trade-offs between different modeling approaches for various analytical and operational use cases."} - {"name":"Cloud Data Infrastructure","description":"Strong technical foundation in modern data infrastructure and cloud platforms. Experience with data warehouse solutions, data lakes, and distributed computing frameworks. Familiarity with Google Cloud Platform infrastructure components and modern data stack tools."} - {"name":"Data Quality and Governance","description":"Understanding of data quality frameworks, data lineage tracking, metadata management, and governance best practices. Ability to implement monitoring, testing, and validation mechanisms to ensure data reliability and compliance."} ### experience - {"name":"7+ Years Data Engineering Experience","description":"Minimum seven years of professional experience in data engineering roles, with substantial focus on ETL pipeline development, data infrastructure, and data warehousing. Track record of designing systems that scale to handle billions of records and support mission-critical business operations."} - {"name":"Experience with Distributed Teams","description":"Demonstrated ability working effectively with geographically distributed engineering teams across multiple time zones. Strong communication and collaboration skills essential for asynchronous coordination and knowledge sharing."} ## Skills ### required - {"name":"Apache Spark","description":"Production-level expertise in Spark for distributed data processing, DataFrame operations, SQL queries, and performance optimization. Experience tuning Spark clusters and debugging complex distributed computing issues."} - {"name":"Databricks","description":"Hands-on experience with Databricks platform, including workspace management, cluster configuration, Delta Lake, and integration with modern data pipelines. Understanding of Databricks Unity Catalog for data governance."} - {"name":"DBT (Data Build Tool)","description":"Strong proficiency in DBT for data transformation workflows, including modeling, testing, documentation, and deployment. Experience building modular, version-controlled data transformation code."} - {"name":"SQL","description":"Expert-level SQL skills including complex queries, window functions, CTEs, query optimization, and understanding of execution plans. Ability to write performant SQL for both OLTP and OLAP workloads."} - {"name":"Data Warehousing Architecture","description":"Deep understanding of data warehouse design, dimensional modeling, fact and dimension tables, slowly changing dimensions, and warehouse optimization patterns. Experience with modern data warehouse solutions."} - {"name":"Data Modeling for Analytics and ML","description":"Ability to design data models that effectively support business intelligence, machine learning, and operational use cases. Understanding of feature engineering and preparing data for AI/ML applications."} - {"name":"Problem-Solving and Analysis","description":"Strong analytical mindset with demonstrated ability to break down complex problems, evaluate multiple solutions, and implement optimal approaches. Attention to detail in data quality and correctness verification."} ### preferred - {"name":"Google Cloud Platform (GCP)","description":"Familiarity with GCP services including BigQuery, Cloud Storage, Pub/Sub, Dataflow, and other data infrastructure components. Understanding of GCP's data analytics and machine learning ecosystem."} - {"name":"Apache Airflow","description":"Experience with Apache Airflow or similar workflow orchestration tools for scheduling, monitoring, and managing complex data pipelines. Understanding of DAG design and pipeline dependency management."} - {"name":"Data Infrastructure Tools","description":"Exposure to modern data stack tools such as dbt-core, Great Expectations, Fivetran, Stitch, or other contemporary data engineering tools. Understanding of data integration, quality, and observability platforms."} - {"name":"AI and Machine Learning Fundamentals","description":"Understanding of AI/ML concepts, model requirements, and how to structure data to support machine learning workflows. Interest in practical applications of AI and building data foundations for AI systems and agentic workflows."} - {"name":"Financial Services Domain Knowledge","description":"Background or familiarity with financial technology, payments, banking, or fintech domain. Understanding of regulatory requirements, compliance considerations, and financial data processing patterns."} - {"name":"Real-Time Data Processing","description":"Experience building and maintaining real-time data pipelines, streaming architectures, and event-driven systems. Familiarity with use cases requiring sub-second latency and high-throughput data processing."} ## Tech stack ### tools - {"name":"Apache Airflow","description":"Workflow orchestration platform for scheduling, monitoring, and managing complex interdependent data pipelines. Enables data workflow automation and alerting."} - {"name":"Git and Version Control","description":"Essential for managing data transformation code, collaborating with teams, and maintaining code quality through peer review and CI/CD practices."} - {"name":"Data Quality Tools","description":"Frameworks like Great Expectations, dbt tests, or custom validation solutions for ensuring data accuracy, completeness, and consistency across pipelines."} - {"name":"Monitoring and Observability","description":"Tools for pipeline monitoring, data quality dashboards, alerting, and debugging. Integration with data governance and metadata management systems."} ### others - {"name":"Cloud Infrastructure (Google Cloud Platform)","description":"Modern cloud platform providing data infrastructure services including compute, storage, and analytics. Familiarity with cloud architecture patterns, cost optimization, and managed services."} - {"name":"Data Governance and Lineage","description":"Understanding of metadata management, data catalog systems, data lineage tracking, and governance frameworks. Important for maintaining data quality and compliance in large organizations."} - {"name":"Dimensional Modeling and Star Schema","description":"Deep expertise in dimensional modeling techniques, fact tables, dimension tables, conformed dimensions, and slowly changing dimensions for business intelligence."} - {"name":"Real-Time Event Processing","description":"Expertise in building event-driven architectures, real-time data pipelines, stream processing, and handling high-volume event data with low latency requirements."} ### databases - {"name":"Google BigQuery","description":"Cloud-native data warehouse platform supporting SQL queries, real-time streaming ingestion, and machine learning integration. Primary analytics database likely used at Airwallex."} - {"name":"PostgreSQL","description":"Robust relational database system supporting complex queries, ACID compliance, and advanced data modeling. Common choice for operational and dimensional data."} - {"name":"Data Lakes","description":"Storage infrastructure for raw and processed data supporting both structured and unstructured formats. Experience with cloud object storage (e.g., Google Cloud Storage) and Delta Lake format."} ### languages - {"name":"SQL","description":"Primary language for data querying, transformation, and analysis across data warehouses and data lakes. Critical for ETL pipeline development and data modeling."} - {"name":"Python","description":"Used for data pipeline orchestration, ETL script development, data quality frameworks, and integration with machine learning workflows. Essential for complex data transformations and automation."} - {"name":"Scala","description":"Optional language for Spark development and optimization, providing performance benefits for distributed computing tasks in data engineering workflows."} ### frameworks - {"name":"Apache Spark","description":"Distributed data processing framework for large-scale ETL pipelines, batch processing, and streaming analytics. Core technology for scalable data transformations."} - {"name":"Databricks","description":"Unified analytics platform built on Apache Spark, providing managed infrastructure for data engineering, machine learning, and analytics workloads."} - {"name":"DBT (Data Build Tool)","description":"Data transformation framework enabling version-controlled, testable, and documented SQL-based transformations. Supports data modeling and pipeline orchestration."} - {"name":"Apache Flink","description":"Stream processing framework for real-time data pipeline development, supporting complex event processing and low-latency transformations."} - {"name":"Apache Kafka","description":"Distributed event streaming platform for real-time data ingestion, enabling event-driven architectures and streaming ETL pipelines."} ## Benefits ### benefits - {"name":"Equity and Stock Options","description":"Participate in Airwallex's success through competitive equity compensation and stock options as part of your total rewards package, aligning your interests with long-term company growth and value creation."} - {"name":"Comprehensive Health Coverage","description":"Access to comprehensive health insurance plans including medical, dental, and vision coverage. Wellness programs and preventative health benefits supporting employee wellbeing."} - {"name":"Professional Development","description":"Learning and development opportunities including training budgets, conference attendance, mentorship programs, and access to specialized courses in data engineering, cloud platforms, and emerging technologies."} - {"name":"Global Work Environment","description":"Collaborate with talented engineers and professionals across 27 offices worldwide. Exposure to diverse markets, regulatory environments, and technical challenges across North America, Europe, Middle East, and Asia-Pacific."} - {"name":"Flexible Benefits","description":"Flexible work arrangements where applicable, competitive time-off policies, and wellness programs. Support for work-life balance and employee wellbeing initiatives."} - {"name":"Career Growth at Scale","description":"Opportunity to grow your career within a high-growth fintech organization serving 675,000+ businesses. Work on mission-critical systems that power global commerce and financial operations at scale."} - {"name":"Financial Services Innovation","description":"Contribute to cutting-edge AI-native financial infrastructure. Work on challenging technical problems in payments, risk management, and real-time financial data processing that directly impact global commerce."} ## Compensation - **max:** 220000 - **min:** 150000 - **currency:** SGD - **stockOptions:** true ## Interview process ### steps ## Full description ## **About Airwallex** Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own. We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our[ operating principles](https://www.airwallex.com/en-us/operating-principles) to see it in full. ## How you'll make impact You'll build the data foundations that power Airwallex's Customer Data Platform, helping teams turn customer information into trusted, reusable data for analytics, machine learning, operational decision-making, and practical AI applications. * Own scalable ETL pipelines across batch and real-time workflows, using technologies such as Databricks, DBT, and Spark * Build reliable data foundations across data warehouses, data consumption platforms, and modern cloud data infrastructure * Develop data models and data products that support operational needs, agentic workflows, machine learning, and business intelligence including star, snowflake, normalised, and denormalised designs * Apply data engineering and AI best practices to solve practical problems in creative and scalable ways * Partner with globally distributed teams to deliver complex technical work with strong problem-solving, attention to detail, communication, and collaboration This is an in-office role — you'll be in our Singapore office five days a week. ## What we're looking for ### Essentials * A bachelor’s degree or higher in Computer Science, Information Systems, Mathematics, or a related field * At least seven years of experience designing and implementing scalable ETL pipelines, including batch and streaming workflows * Experience with streaming technologies such as Apache Kafka or Apache Flink * Strong proficiency in SQL, database management systems, data warehousing, and data modelling for business intelligence, machine learning, and operational needs * Solid understanding of data schemas, including star, snowflake, normalised, and denormalised designs * Excellent analytical and problem-solving abilities, attention to detail, and effective communication and collaboration skills * An understanding of AI and an interest in applying data engineering and AI in practical and creative ways ### Preferred * Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow * Experience working with globally distributed teams ## Learn more about your team The Customer Data Platform team is building Airwallex’s trusted customer-data foundation—unifying customer entities, signals and metrics so that teams, products and AI systems can make better decisions and take action. ## **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_](http://airwallex.com) _email address. Please apply only through_ [_careers.airwallex.com_](http://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.