Top Data Engineer Jobs in Singapore

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.

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.

Assist in building and maintaining data ingestion pipelines and storage for AI workflows. Implement deployment, security, and monitoring for applications; collaborate with stakeholders and design teams; test and validate data verification and AI workflows; document solutions and follow best practices while continuously learning new tools.

Design and build full-stack, cloud-native enterprise applications that manage corporate data from ingestion through serving. Develop APIs, integrations, data pipelines, CI/CD pipelines, observability, and secure data synchronization across networks. Participate in agile sprints, code reviews, documentation, issue resolution, and technical knowledge sharing. The role may also involve integrating LLM and agentic AI capabilities.

Leads data-driven industrial optimization projects from requirements gathering through deployment and monitoring. Responsibilities include building scalable data pipelines, integrating industrial data from IoT devices, sensors, test benches, and SCADA systems, performing data quality checks and exploratory analysis, developing machine learning models, creating Power BI and Flask dashboards, supporting digital twin simulations, and developing RAG-based chatbot solutions.

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.

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 and build scalable cloud-native data pipelines and lakehouse solutions using Spark, PySpark, Apache Iceberg, streaming technologies, Python, shell scripting, and SQL. Modernize legacy workloads, migrate systems to cloud architectures, implement data quality and monitoring automation, and support metadata, lineage, and governance through open catalogue frameworks. Collaborate with analysts, data scientists, and stakeholders to deliver reliable, high-performance data solutions.

Design and build cloud-native data pipelines, lakehouse solutions, and real-time streaming systems using Spark, PySpark, Iceberg, Python, and SQL. Modernize legacy workloads, migrate processing systems to cloud architectures, automate ETL/ELT workflows, and implement data quality, monitoring, governance, metadata, and lineage practices. Collaborate with analysts, data scientists, and stakeholders to deliver reliable, scalable data solutions and troubleshoot pipeline performance and operational issues.

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.

Lead the design and delivery of enterprise-scale data platforms and pipelines, using modern data platforms, processing frameworks, streaming technologies, and cloud services. Build, lead, and mentor a data engineering team; support client relationships, proposals, migrations, governance, security, and compliance. Contribute technical thought leadership through blogs and community engagement while delivering solutions from proof of concept through commercialization.

Design and build enterprise-scale ingestion frameworks and Lakehouse platforms. Develop reusable Spark/Python components, data models, data products, quality controls, and observability. Support CI/CD, DevOps, Kubernetes-based deployments, and operationalize ML models for AI-driven analytics and risk/fraud use cases in banking.

Design, train, evaluate and deploy machine learning and AI models. Build data pipelines, preprocess data, run EDA, optimise algorithms, deploy models to production (cloud/APIs/containers), monitor performance, document models, and collaborate with cross-functional teams on AI prototypes and production solutions.

Develop Green AI technologies focused on efficient machine learning, large language models, model optimization, parameter-efficient fine-tuning, retrieval-augmented generation, prompt engineering, and efficient inference. Conduct experiments, benchmark algorithms, analyze performance, contribute to research publications, and collaborate with multidisciplinary teams to translate research into deployable sustainable computing solutions.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

The Data Engineer designs and maintains data pipelines and systems, transforming raw data into insights by collaborating with project teams and utilizing various data processing tools.

Design, build and operate scalable Databricks/PySpark data pipelines to ingest, transform and validate financial vendor data into production-ready datasets, partnering with product and client teams to meet quality and timeline requirements.

Design, build, secure, and operate highly available data centre networks, including leaf-spine fabrics, routing, switching, firewalls, load balancers, segmentation, and inter-data-centre connectivity. Troubleshoot mission-critical network issues, improve reliability, and manage capacity and performance. Develop Network-as-Code automation for provisioning, validation, testing, compliance, and drift detection. Apply telemetry, AI, and AIOps techniques while collaborating with cloud, compute, storage, cybersecurity, and application teams.

Design, build, and scale data infrastructure for LLM training, fine-tuning, evaluation, and RAG. Implement and maintain data architectures, pipelines (batch and streaming), org-level IAM and governance, cost/performance optimizations, analytics/dashboarding, AI-assisted ops and internal automation tools, and support multi-cloud production workloads.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.