# Senior Data Engineer, Data and AI, Customer Data Platform **Company:** [Airwallex](https://scaleengineer.com/companies/airwallex) Senior Data Engineer role at Airwallex building scalable ETL pipelines and data foundations for the Customer Data Platform. This position requires 5+ years of experience with batch and streaming data technologies, expertise in SQL, data warehousing, and data modeling, plus practical AI knowledge to support analytics, machine learning, and operational decision-making across a globally distributed fintech organization. **Role:** Data Engineer **Seniority:** Senior **Locations:** SG - Singapore **Salary:** 130000–180000 SGD [Apply](https://jobs.ashbyhq.com/airwallex/aa8f06d1-cda8-4405-b852-963ed89445a9) Canonical: https://scaleengineer.com/jobs/airwallex/senior-data-engineer-data-and-ai-customer-data-platform --- ## Responsibilities - Design and Implement Scalable ETL Pipelines: Own end-to-end responsibility for building and maintaining ETL pipelines that handle both batch and real-time data workflows. Use technologies like Databricks, DBT, and Apache Spark to process data at scale while ensuring reliability, performance optimization, and adherence to SLAs. Work with large volumes of customer financial data while maintaining data quality standards and implementing comprehensive monitoring and alerting mechanisms. - Architect Data Foundations and Infrastructure: Build robust data warehouses, data consumption platforms, and modern cloud data infrastructure that support multiple downstream use cases. Design systems that enable reliable data access for analytics teams, machine learning engineers, and operational stakeholders. Implement data governance frameworks, access controls, and documentation standards to ensure data trustworthiness across the organization. - Develop Advanced Data Models and Products: Create sophisticated data models supporting diverse business requirements including star schemas, snowflake schemas, normalized, and denormalized designs. Build data products that enable operational decision-making, support agentic AI workflows, power machine learning features, and drive business intelligence insights. Collaborate with stakeholders to understand evolving data needs and iterate on models accordingly. - Implement Real-Time Streaming Solutions: Develop and optimize streaming data architectures using Apache Kafka, Apache Flink, or similar technologies to enable real-time data availability. Build event-driven data pipelines that support immediate operational decisions and AI-powered actions. Ensure low-latency data delivery while maintaining data consistency and fault tolerance in production environments. - Apply Data Engineering and AI Best Practices: Implement industry best practices for data engineering, focusing on code quality, testing frameworks, and documentation. Apply AI and machine learning principles to solve practical business problems in creative and scalable ways. Stay current with emerging data technologies and AI methodologies, contributing to technical strategy and architectural decisions. - Collaborate with Distributed Global Teams: Partner with globally distributed engineering, data science, product, and business teams to deliver complex technical initiatives. Provide clear technical communication, mentorship, and knowledge sharing. Lead design discussions, conduct code reviews, and contribute to architectural decisions that impact multiple teams and systems. - Ensure Data Quality and Reliability: Implement comprehensive data quality frameworks, testing strategies, and validation mechanisms. Monitor pipeline health, troubleshoot issues, and implement solutions to prevent data inconsistencies. Build automated data quality checks and establish SLAs for data availability and accuracy. ## Requirements ### education - {"name":"Bachelor's Degree in Computer Science, Information Systems, or Related Field","description":"A bachelor's degree or higher qualification in Computer Science, Information Systems, Mathematics, Engineering, Statistics, or a closely related discipline. Advanced degrees such as Master's are a plus but not required."} ### technical - {"name":"ETL Pipeline Design and Implementation","description":"Proven expertise designing and implementing scalable ETL pipelines handling both batch and streaming workflows at enterprise scale, with experience optimizing for performance, reliability, and maintainability."} - {"name":"Streaming Data Technologies","description":"Strong hands-on experience with streaming platforms such as Apache Kafka or Apache Flink, including topics like event streaming architecture, partitioning strategies, fault tolerance, and real-time data delivery."} - {"name":"SQL and Database Management","description":"Advanced SQL proficiency including complex queries, query optimization, window functions, and CTEs. Deep understanding of relational database management systems, indexing strategies, and performance tuning."} - {"name":"Data Warehousing Architecture","description":"Comprehensive knowledge of data warehouse design, including dimensional modeling approaches, slowly changing dimensions (SCDs), incremental loading strategies, and query optimization for analytical workloads."} - {"name":"Data Modeling and Schema Design","description":"Expert-level understanding of data schemas including star schemas, snowflake schemas, normalized designs for transactional systems, and denormalized designs for analytical systems. Ability to select appropriate approaches based on use case requirements."} - {"name":"Cloud Data Platforms","description":"Experience with modern cloud data infrastructure, particularly Google Cloud Platform data services, including BigQuery, Cloud Storage, and data integration tools. Understanding of cloud-native data architecture patterns."} - {"name":"Data Processing Frameworks","description":"Proficiency with Apache Spark, DBT (Data Build Tool), and similar frameworks for building reproducible, maintainable data transformation pipelines. Experience with distributed processing concepts and optimization techniques."} - {"name":"AI and Machine Learning Fundamentals","description":"Understanding of AI/ML concepts and ability to apply data engineering principles to support machine learning workflows, including feature engineering, data preparation, and pipeline orchestration for model training and inference."} ### experience - {"name":"5+ Years Data Engineering Experience","description":"Minimum five years of professional experience in data engineering, designing and implementing ETL pipelines, data warehousing, or related data infrastructure roles at technology companies or organizations with significant data complexity."} - {"name":"Batch and Streaming Pipeline Development","description":"Demonstrated experience building both batch processing pipelines for periodic data processing and streaming pipelines for real-time data ingestion and processing in production environments."} - {"name":"Cross-Functional Collaboration","description":"Proven ability to work effectively with distributed teams across geographies, time zones, and organizational functions. Experience partnering with analytics, data science, engineering, and business stakeholders to understand and implement data requirements."} - {"name":"Problem-Solving and Attention to Detail","description":"Strong analytical capabilities with a track record of identifying root causes, proposing scalable solutions, and implementing robust fixes. Demonstrated attention to data accuracy, consistency, and quality in complex systems."} ## Skills ### required - {"name":"SQL","description":"Advanced SQL for complex data transformations, query optimization, and analytical queries. Must be comfortable writing efficient queries against large datasets and understanding execution plans."} - {"name":"Apache Spark","description":"Hands-on proficiency with Spark for distributed data processing, including RDDs, DataFrames, and Spark SQL. Experience optimizing Spark jobs for performance and cost efficiency."} - {"name":"Apache Kafka or Apache Flink","description":"Production experience with Kafka for event streaming architectures or Flink for complex stream processing, including handling backpressure, state management, and exactly-once semantics."} - {"name":"Data Warehouse Design","description":"Expertise in designing data warehouses and implementing dimensional models, understanding slowly changing dimensions, fact and dimension tables, and analytical query optimization patterns."} - {"name":"DBT (Data Build Tool)","description":"Experience using DBT for building modular, version-controlled data transformation pipelines. Knowledge of documentation, testing, and best practices for managing data models as code."} - {"name":"Cloud Platforms (GCP, AWS, or Azure)","description":"Hands-on experience with cloud data services on at least one major platform, such as Google Cloud Platform (BigQuery, Dataflow), AWS (Redshift, Glue), or Azure (Synapse, Data Factory)."} - {"name":"Data Modeling","description":"Strong ability to design data schemas using star schema, snowflake schema, normalized, and denormalized approaches based on specific business and technical requirements."} - {"name":"Debugging and Troubleshooting","description":"Expert-level ability to diagnose and resolve data pipeline issues, analyze logs, and implement monitoring and alerting for production data systems."} ### preferred - {"name":"Databricks","description":"Experience with Databricks for unified data analytics and AI, including Delta Lake for ACID transactions, and using Databricks as a collaborative platform for data engineers, data scientists, and analysts."} - {"name":"Apache Airflow","description":"Familiarity with Airflow for workflow orchestration, DAG management, and scheduling complex data pipelines across distributed systems."} - {"name":"Google Cloud Platform Expertise","description":"Deep knowledge of GCP data services including BigQuery, Cloud Storage, Pub/Sub, Dataflow, and understanding of GCP's ecosystem for modern data infrastructure."} - {"name":"AI/ML Pipelines","description":"Experience building data pipelines that support machine learning workflows, including feature stores, model training data preparation, and inference data pipelines."} - {"name":"FinTech or Financial Services Domain Knowledge","description":"Background in financial technology, banking, or payments industries, with understanding of financial data models, compliance requirements, and operational considerations specific to financial institutions."} - {"name":"Python Programming","description":"Proficiency in Python for building custom data processing applications, orchestration scripts, and data quality frameworks."} - {"name":"Version Control and CI/CD","description":"Experience with Git version control and continuous integration/continuous deployment practices for data pipelines, ensuring code quality and reliable deployments."} - {"name":"Global Team Collaboration","description":"Proven experience working with geographically distributed teams, managing asynchronous communication, and contributing to global technical initiatives."} ## Tech stack ### tools - {"name":"Databricks","description":"Unified analytics platform built on Apache Spark, providing collaborative environment for data engineers, data scientists, and analysts. Combines data engineering, analytics, and AI capabilities."} - {"name":"Apache Airflow","description":"Workflow orchestration platform for scheduling, monitoring, and managing complex data pipelines. Enables reliable pipeline execution with dependency management and error handling."} - {"name":"Google Cloud Platform (GCP)","description":"Cloud infrastructure provider offering data warehousing, data processing, storage, and analytics services. Strategic platform for Airwallex's data infrastructure."} - {"name":"Git and Version Control","description":"Version control system for managing data pipeline code, enabling collaboration, code review, and maintaining deployment history."} ### others - {"name":"Data Modeling Techniques","description":"Star schema, snowflake schema, normalized and denormalized designs for different analytical and operational use cases."} - {"name":"Real-Time Data Architecture","description":"Event-driven architecture patterns, stream processing semantics, and operational data management for AI and agentic workflows."} - {"name":"Data Governance and Quality","description":"Data quality frameworks, data lineage tracking, access control implementation, and data governance practices for maintaining data trustworthiness."} - {"name":"Pipeline Monitoring and Observability","description":"Implementation of monitoring, logging, alerting, and observability for data pipelines to ensure reliability and quick issue resolution in production."} ### databases - {"name":"Google BigQuery","description":"Serverless, cloud-native data warehouse providing fast SQL queries at scale. Primary data warehouse platform for storing and analyzing customer data."} - {"name":"Delta Lake","description":"Open-source storage format providing ACID transactions and unified batch and streaming semantics. Ensures data reliability and consistency in data lakes."} - {"name":"Relational Databases (PostgreSQL, MySQL, Oracle)","description":"Understanding of relational database systems for source system data extraction and dimensional modeling principles applicable across platforms."} ### languages - {"name":"SQL","description":"Core language for data transformation, querying, and analysis. Used extensively for ETL pipeline development and data model implementation."} - {"name":"Python","description":"Used for building custom data processing applications, orchestration scripts, and data quality frameworks. Industry standard for data engineering in modern tech companies."} ### frameworks - {"name":"Apache Spark","description":"Distributed data processing framework for building scalable ETL pipelines. Core technology for handling large-scale batch and streaming data processing."} - {"name":"DBT (Data Build Tool)","description":"Data transformation framework that enables version control, testing, and documentation of data models. Treats data transformations as code for improved maintainability."} - {"name":"Apache Kafka","description":"Distributed event streaming platform for real-time data ingestion and processing. Essential for building streaming data pipelines and event-driven architectures."} - {"name":"Apache Flink","description":"Stream processing framework for real-time data transformation and complex event processing. Alternative or complement to Kafka for advanced streaming requirements."} ## Benefits ### benefits - {"name":"Equity Compensation","description":"Stock options or equity grants as part of the comprehensive compensation package, aligning employee interests with company growth as Airwallex scales its fintech operations globally."} - {"name":"Professional Development","description":"Access to training, conferences, and continuing education opportunities to stay current with emerging data technologies, cloud platforms, and AI/ML advancements in the data engineering field."} - {"name":"Collaborative Work Environment","description":"Five days per week in Airwallex's modern Singapore office with world-class facilities, fostering collaboration across the globally distributed 2,300+ person team and providing access to leadership and cross-functional partners."} - {"name":"Career Growth Opportunities","description":"Opportunities to advance within a rapidly growing fintech organization with clear paths to staff engineer, technical lead, or management roles. Exposure to cutting-edge financial technology and AI applications."} - {"name":"Diverse Project Exposure","description":"Work on high-impact projects supporting 675,000+ businesses including McLaren Racing, Qantas, SHEIN, and TikTok. Experience building infrastructure for global commerce and real-time financial operations."} - {"name":"Global Impact","description":"Contribute to regulated financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. Work on systems processing billions in cross-border transactions affecting millions of users globally."} ## Compensation - **max:** 180000 - **min:** 130000 - **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 five 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.