Top Data Engineer Jobs in Singapore

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

Define and own Finance data architecture and pipelines from ingestion to reporting with full lineage and immutability. Ensure audit-ready financial reporting, partner with Accounting/Treasury for month-end and statutory reporting, build embedded data quality controls, lead incident response, drive AI/LLM adoption in data workflows, mentor engineers, and represent the domain across the organization.

Own end-to-end compliance data pipelines from ODS ingestion through CDM to downstream marts for TMS, KYC, CMS, and regulatory reporting. Build production-grade, auditable pipelines with proactive data quality, monitoring, and reconciliation. Use AI/LLM tooling to accelerate domain ramp-up and pipeline development. Collaborate with Compliance Project Lead, take sub-domain ownership, and mentor junior engineers.

Develop and maintain Python libraries, services, and frameworks for data pipelines used in trading and research. Collaborate with quantitative and tech teams to design data models, deploy resilient data platform tooling, improve development velocity, write design/requirements docs and runbooks, and provide higher-order production support during business hours.

The Senior/Lead Data Engineer designs and optimizes data solutions on Alibaba Cloud, develops data pipelines, manages data warehouses, and ensures data quality and compliance.

The Platforms and Data Engineer will develop and manage high-performance analytics solutions, design data transformation processes, maintain data systems, and support AI projects.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Design, build, and maintain analytics-ready data marts using dimensional modeling; partner with analytics and business teams to deliver documented datasets; implement resilient batch and real-time data pipelines; architect cloud data infrastructure; enforce data standards, validation, and monitoring to ensure data integrity and scalability.

Build and maintain the risk-control data warehouse, develop thematic metrics and indicators, design offline and real-time data architectures, optimize data pipelines, and deliver reporting and API interfaces for the data mart to support risk control business lines.

Design, develop, and maintain data infrastructure microservices using C++, optimize for performance, debug issues, and collaborate with teams.

Lead mechanical design and engineering for data centre projects, manage multidisciplinary teams and budgets, engage clients, mentor junior engineers, and integrate innovative, energy-efficient solutions.

Support and optimize real-time financial market data systems, including exchange and client feeds. Integrate, normalize, monitor, and distribute low-latency market data while troubleshooting connectivity, data integrity, and infrastructure issues. Develop automation, tune system performance, conduct capacity planning, maintain documentation, and ensure compliance with exchange and regulatory requirements. The role also supports deployments and production incidents during weekends and public holidays.

Implement and configure Collibra data catalog and lineage (technical and business), integrate metadata from ETL, data lake/DWH and core banking systems, map end-to-end lineage, enforce MAS-aligned data governance, validate metadata quality, and manage stakeholder delivery and reporting.

Design, build and operate enterprise-scale data and analytics platforms on GCP. Lead architecture, reliability and incident response for BigQuery/Dataproc/Spark workloads, implement IaC and CI/CD, drive SRE/observability, optimize performance and cost, mentor engineers and enforce security and operational best practices.

Plan and execute end-to-end data transformation and migration for large, complex datasets. Lead extraction, validation, reconciliation, mock runs, trial loads, and final cutover while ensuring traceability, audit compliance, and database performance in high-scrutiny government-linked environments.

Operate, maintain, and optimize hybrid cloud Data and AI platforms (Cloudera CDP/CML, Hadoop, Spark) across AWS and on-prem. Perform platform upgrades, security patching, container/Kubernetes/OpenShift administration, monitoring, troubleshooting, and automation using Shell/Python. Collaborate with Infrastructure, Security, SRE, and Data/ML teams to ensure availability and scalability of AI/ML and Generative AI services (SageMaker, EMR, Bedrock).

Develop and implement computer vision algorithms, evaluate model performance, and integrate solutions into larger systems, while maintaining documentation and staying updated on technology advances.

The Senior Data Engineer will lead AI Data Engineering initiatives, architect and manage data infrastructure, and optimize data pipelines for AI and ML applications using various tools and technologies.

Lead semiconductor manufacturing analytics: own test analytics strategy, build analytics platforms, implement quality screening (PAT, SBL, outlier detection), integrate foundry/OSAT/MES systems, design data infrastructure and automation (ETL, dashboards), and drive operational excellence across wafer, assembly, test, and quality.

Ensure reliability, availability, and performance of data centre IT operations. Manage day-to-day monitoring, incident lifecycle, capacity planning, documentation, observability and network monitoring, remote management tools, and define SRE metrics (SLO/SLI/error budgets). Collaborate with facilities on power, cooling, and physical infrastructure.

Build, test, and deploy scalable, data-intensive web search and analytics systems for national-security use. Design architectures, write maintainable code and tests, monitor system health, optimise performance, deliver proofs-of-concept, and support mission-critical deployments.

Lead design, build and maintain scalable, low-latency large-scale data pipelines. Optimize performance, ensure data quality and governance, troubleshoot critical systems, mentor junior engineers, and collaborate with stakeholders to deliver mission-critical data products.

Design, develop, test, and maintain scalable data processing applications and APIs. Build and operate data pipelines, improve system resiliency, participate in code reviews, document solutions, and prepare production-ready data for AI applications while collaborating within an Agile team.

Design, build, and maintain full-stack applications and data pipelines to enable data discovery and sensemaking. Integrate modern technologies, debug and optimize systems, participate in code/design reviews, and communicate solutions to stakeholders.