Top Data & Analytics Jobs

Leads a data engineering team responsible for data modeling, batch and streaming ETL pipelines, analytics-ready datasets, data governance, and data quality. Hires, coaches, and manages engineers while setting technical direction and partnering with engineering, product, and business stakeholders. Oversees scalable data foundations across regulatory, customer, business, content, and growth domains, and advances practical AI automation and AI-ready infrastructure.

Establish and lead a global customer insights function: own NPS/VoC, synthesize qualitative and quantitative signals, drive competitive intelligence, build dashboards with Looker/Databricks/SQL, partner with Product and GTM, hire and manage a distributed team, and apply AI to scale research and insight workflows.

Design and build scalable batch and streaming ETL pipelines and data models, collaborate with stakeholders to translate data needs, ensure data quality and governance, and own end-to-end delivery for Knowledge Platform initiatives in Singapore.

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform strategy, build scalable multi-region data pipelines, promote best practices, mentor engineers, and enable data-driven decision making and AI readiness across the organization.

Design and build scalable batch and streaming ETL pipelines and data models. Partner with engineers and business stakeholders to deliver reliable SSOT data, ensure data quality, governance, and support ML/AI use cases. Own end-to-end pipeline design, monitoring, and ongoing improvements.

Lead user research and market insights to evaluate customer perception of Airwallex. Design qualitative and quantitative studies, synthesize feedback, present data-driven recommendations to leadership, and collaborate with product, engineering, and commercial teams to integrate insights and improve customer experience.

Lead design, build, and operate distributed, large-scale (batch/streaming) data platforms and pipelines. Drive technical decisions, performance optimization, data architecture (lakehouse), tooling standards, third-party integration, and mentor junior engineers to enable company-wide data-driven capabilities.

Lead architecture and delivery of a governed Data→Knowledge→Skills platform on Databricks, ensuring data quality, lineage, semantic definitions, regional data localization, and reusable programmatic capabilities for analysts and AI agents.