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 200,000 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 2,000 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 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. Senior Data Engineer Hiring Location: Singapore What you'll do Part 1. Data Modeling - Design and implement robust and scalable data models that support business intelligence, machine learning, and operational needs. - Possess a deep understanding of data schemas and be able to select appropriate schema designs (e.g., star schema, snowflake, normalized vs denormalized) based on use cases. - Collaborate closely with business teams to translate their data needs into clean, structured, and well-documented models. - Understand and promote the concept of SSOT (Single Source of Truth) throughout the data layers and pipelines. - Maintain data consistency, traceability, and quality across multiple data sources and domains. Part 2. ETL and Data Pipeline Management - Experience building and maintaining both batch and streaming ETL pipelines, with a strong understanding of end-to-end data workflow - from data ingestion to transformation and delivery. - Able to work closely with Data Platform Engineers (DPEs) and Product Managers (PMs) to quickly identify root causes of data issues and provide efficient, scalable solutions. - Bonus if you've worked with data across distributed or multi-datacenter systems, including solving challenges related to data migration, duplication, and consistency. Part 3. Data Governance - Participate in and contribute to data governance strategies, policies, and standards. - Be familiar with any of the six key pillars of traditional data governance (e.g., data quality, data stewardship, metadata management, master data management, data privacy/security, data lifecycle). Part 4. Data + AI - We hope you have a basic understanding of AI and enjoy thinking about how data engineering and AI can work together in practical and creative ways. These four areas are the main focuses of the DE team. Ideally, you should be strong in at least one of them - especially data modeling or data ETL. If you also have experience or skills in the other areas, that would be a big plus. 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 qualifications: - Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or a related field. - Minimum 7 years of proven experience designing and implementing ETL pipelines, with a strong understanding of the strategies, rules, and processes involved in building scalable and reliable data pipelines. - Experience with streaming data pipelines or real-time data processing technologies, such as Apache Kafka or Apache Flink. - Proficiency in SQL, database management systems (e.g., MySQL, PostgreSQL, Oracle), and data warehousing solutions. - Familiarity with Google Cloud Platform (GCP) and modern data infrastructure tools, including Databricks and Airflow. - Knowledge of data governance practices and regulatory requirements within the financial industry. - Excellent problem-solving skills with strong attention to detail and a commitment to producing high-quality work. - Strong communication and collaboration skills, with the ability to work effectively in a fast-paced, team-oriented environment. - Outstanding verbal communication skills, with the ability to collaborate effectively with globally distributed teams. Preferred qualifications: - Experience with financial industries, payment systems, or fintech platforms. - Knowledge of data governance practices and regulatory requirements in the financial industry. - Experience with scripting languages (e.g., Python, R) for data analysis and automation. - Certification in data management or related technologies At Airwallex, you can make an impact in a rapidly growing, global fintech. We want you to share in our success, which is why you'll be offered a competitive salary plus valuable equity within Airwallex. We also like to ensure we create the best environment for our people by providing collaborative open office space with a fully stocked kitchen. We organise regular team-building events and we give our people the freedom to be creative. 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.

Job Title: Data Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for pipeline monitoring and data refresh cycles) About the Role: Our client is seeking a Data Engineer to design, build, and maintain reliable data pipelines and infrastructure that deliver clean, accessible, and actionable data. This role requires strong software engineering fundamentals, experience with modern data stacks, and an eye for quality and scalability. The Data Engineer ensures data flows seamlessly from source systems to warehouses and BI tools, powering decision-making across the business. Responsibilities: Pipeline Development: - Build and maintain ETL/ELT pipelines using Python, SQL, or Scala. - Orchestrate workflows with Airflow, Prefect, Dagster, or Luigi. - Ingest structured and unstructured data from APIs, SaaS platforms, relational databases, and streaming sources. Data Warehousing: - Manage data warehouses (Snowflake, BigQuery, Redshift). - Design schemas (star/snowflake) optimized for analytics. - Implement partitioning, clustering, and query performance tuning. Data Quality & Governance: - Implement validation checks, anomaly detection, and logging for data integrity. - Enforce naming conventions, lineage tracking, and documentation (dbt, Great Expectations). - Maintain compliance with GDPR, HIPAA, or industry-specific regulations. Streaming & Real-Time Data: - Develop and monitor streaming pipelines with Kafka, Kinesis, or Pub/Sub. - Ensure low-latency ingestion for time-sensitive use cases. Collaboration: - Partner with analysts and data scientists to provide curated, reliable datasets. - Support BI teams in building dashboards (Tableau, Looker, Power BI). - Document data models and pipelines for knowledge transfer. Infrastructure & DevOps: - Containerize data services with Docker and orchestrate in Kubernetes. - Automate deployments via CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI). - Manage cloud infrastructure using Terraform or CloudFormation. What Makes You a Perfect Fit: - Passion for clean, reliable, and scalable data. - Strong problem-solving skills with debugging mindset. - Balance of software engineering rigor and data intuition. - Collaborative communicator who thrives in cross-functional environments. Required Experience & Skills (Minimum): - 3+ years in data engineering or back-end development. - Strong Python and SQL skills. - Experience with at least one major data warehouse (Snowflake, Redshift, BigQuery). - Familiarity with pipeline orchestration tools (Airflow, Prefect). Ideal Experience & Skills: - Experience with dbt for transformations and data modeling. - Streaming data experience (Kafka, Kinesis, Pub/Sub). - Cloud-native data platforms (AWS Glue, GCP Dataflow, Azure Data Factory). - Background in regulated industries (healthcare, finance) with strict compliance. What Does a Typical Day Look Like? A Data Engineer’s day revolves around keeping pipelines running, improving reliability, and enabling teams with high-quality data. You will: - Check pipeline health in Airflow/Prefect and resolve any failed jobs. - Ingest new data sources, writing connectors for APIs or SaaS platforms. - Optimize SQL queries and warehouse performance to reduce costs and latency. - Collaborate with analysts/data scientists to deliver clean datasets for dashboards and models. - Implement validation checks to prevent downstream reporting issues. - Document and monitor pipelines so they’re reproducible, scalable, and audit-ready. In essence: you ensure the business has accurate, timely, and trustworthy data powering every decision. Key Metrics for Success (KPIs): - Pipeline uptime ≥ 99%. - Data freshness within agreed SLAs (hourly, daily, weekly). - Zero critical data quality errors reaching BI/analytics. - Cost-optimized queries and warehouse performance. - Positive feedback from data consumers (analysts, scientists, leadership). Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Task (e.g., build a small ETL pipeline or optimize a SQL query) - Client Interview with Engineering/Data Team - Offer & Background Verification

• Design and implement robust data architectures supporting structured and unstructured data from internal and external sources • Build and maintain scalable, secure data pipelines using cloud-based and distributed technologies • Establish data structures and routing mechanisms based on business and technical requirements • Develop and execute automated test cases to validate ETL workflows, data pipelines, and reporting logic • Create regression test suites to ensure ongoing data integrity and system stability • Analyze current and historical performance data to identify trends, variances, and opportunities • Collaborate with data consumers, project managers, and business stakeholders to define logical and physical database designs for analytics models • Identify and implement improvements in internal data management and testing processes

• Work directly on storage infrastructure, product launches, and new customer experiences built on one of the most advanced AI systems in the world • Collaborate daily with researchers and engineers who are some of the best in the world at what they do • Run implementations end-to-end and see initiatives through to real outcomes — no waiting around to be told what to do • Partner across research, marketing, sales, and finance to help define how Cohere grows, with your recommendations feeding directly into products and strategy