Top Data Engineer Jobs in Singapore

Design, build, test, deploy, and maintain scalable, resilient cloud-based data pipelines and microservices (primarily on AWS). Implement ETL/ELT, data lake/warehouse solutions, support analytics integration, enforce data quality, and follow Agile DevOps practices with CI/CD and IaC.

Lead infrastructure engineering for Market Data Services, architecting and modernizing cloud and on-prem systems. Drive complex programs, validate AI-assisted recommendations, ensure resiliency, security, and compliance, collaborate with stakeholders, provide 3rd-level production support, and adopt automation and monitoring to reduce recurring issues.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform solutions, build scalable multi-region pipelines, enforce governance and security, mentor the team, advocate best practices, and influence technical roadmaps to enable AI/ML-driven business impact.

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 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 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.

Lead advanced data analytics and ML initiatives to improve yield, quality, and reliability for HBM products. Develop ML/DL models, build data infrastructure, integrate solutions into disposition workflows, mentor team members, and collaborate cross-functionally to optimize KPIs including quality, cost, cycle time, and scale.

Design, develop, and deploy large-scale data systems to support cybersecurity products for threat detection, investigation, and analysis. Build scalable, high-availability architectures, microservices, and RESTful APIs with CI/CD. Monitor system health and telemetry, scale systems for growing data volumes, and collaborate with cross-functional teams to meet operational needs.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Lead design and development of scalable data infrastructure and self-service tooling for data engineering teams. Provide technical leadership, implement robust systems (Kubernetes/Terraform/GitOps), ensure quality through testing and maintenance, troubleshoot production issues, and collaborate cross-functionally to deliver reliable data platforms.

Lead design and build of high-performance distributed data infrastructure and AI enablement platforms. Optimize systems internals and JVM performance, implement lakehouse and real-time streaming, develop scalable vector indexing and AI knowledge platform, own SDLC for petabyte-scale systems across global regions, drive engineering best practices, and mentor mid-level engineers.

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.

As a Data Engineer at SHOPLINE, you'll be responsible for managing payments data, enhancing data quality, and constructing business evaluation metrics while collaborating across functions.

Design, build and maintain scalable ETL/ELT data pipelines and integrations on Microsoft Fabric and Databricks. Prepare and model data for AI/ML and analytics, ensure data quality, lineage, security and governance, develop connectors/APIs, support CI/CD deployments, monitor pipelines, and document handovers while collaborating with stakeholders and AI/ML engineers.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, implement, and maintain data-driven processes and analytics solutions. Collect, cleanse, automate, and prepare data from disparate sources. Build KPIs and dashboards, apply statistical and machine learning methods, collaborate with stakeholders, perform testing and deployment, and train users to enable data-driven decision making and process improvements.

Build and maintain end-to-end data pipelines and medallion data lake architectures on Databricks and AWS. Implement data governance with Unity Catalog, ensure data quality, prepare analytics-ready datasets, support basic ML pipelines, and collaborate with stakeholders to meet reporting and BI needs.

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

Develop and deploy product-disposition workflows and deviation alert systems using statistical tools and ML/AI to improve yield, reliability, and quality. Build algorithms for data mining, pattern recognition, and wafer/die screening. Collaborate with Fab, design, and quality teams, mentor team members, and drive technical innovation to meet KPIs like quality, cost, cycle time, and scale.

The role focuses on developing product disposition solutions, enhancing yield and reliability through data analysis, and mentoring team members while collaborating across teams.

The IT Engineer (Data Services) is responsible for managing and optimizing database systems, ensuring data availability, performance, and compliance, while also developing automated solutions and collaborating with site customers.

The IT Engineer (Data Services) is responsible for database system management, performance tuning, compliance, incident resolution, and automation of database operations.

Design, implement, and operate patent and related data pipelines: integrate, parse, store, backup, monitor and optimize data quality and processing performance. Collaborate with data architects to improve scalability, stability, and documentation while ensuring data security and regulatory compliance.

Design, build, and maintain data models, ETL pipelines, dashboards, and APIs to support industry models, research, and consulting. Ensure data accuracy, completeness, and observability while integrating new datasets and cloud infrastructure. Collaborate with analysts and stakeholders to deliver robust, modular production data systems with minimal supervision.

The role involves leading data analytics initiatives to improve yield and quality, developing algorithms, enhancing data infrastructure, and collaborating with cross-functional teams to drive innovation in HBM product engineering.