Top Data Engineer Jobs in Singapore

Design, build, and maintain a unified data framework, scalable data pipelines, and data models. Improve data ingestion, quality, monitoring, and governance; troubleshoot pipeline issues; optimize performance; integrate third-party tools; and apply AI techniques to enhance data quality and engineering workflows.

The Staff Software Engineer will design high-performance backend services, manage distributed state, ensure system resilience, and implement cloud-native architectures with advanced engineering principles.

Design, develop, and deploy production-ready AI applications and scalable DataOps/MLOps pipelines for avionics manufacturing. Build LLM/RAG/agent-based solutions, integrate enterprise and manufacturing systems, implement CI/CD and containerized deployments, ensure security/compliance, and collaborate with stakeholders to deliver measurable operational improvements.

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.

The Software Engineer for Data Infrastructure & Acquisition at Speechify will enhance data collection, manage infrastructures, and work with AI scientists to develop large-scale datasets.

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.

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.

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.

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 and build scalable, metadata-driven data pipelines, frameworks and services for AI/ML use cases. Lead end-to-end SDLC, deliver high-quality backend solutions, manage production support and CI/CD, collaborate with stakeholders, perform POCs, and drive process improvements and automation.

Build, test and support batch and streaming data pipelines and curated datasets on a lakehouse AI data platform. Implement data modelling, partitioning, schema evolution, data quality controls and reconciliation. Collaborate with platform teams and consumers to deliver reliable, scalable data products and reusable tooling; contribute to technical design and mentoring for more senior candidates.

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

Design, validate, and deploy AI algorithms for digital health projects. Lead technical innovation, support data platform development and retrospective analysis, collaborate with clinicians and biostatisticians, publish findings, prepare technical reports, and mentor junior AI researchers.

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

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 backend services and data pipelines to ingest, process, enrich, and deliver large scientific, media, and geospatial datasets. Implement REST APIs, event-driven and streaming systems, and Kubernetes-based production infrastructure to support machine learning and agentic AI applications. Collaborate with scientists and ML engineers to translate requirements into scalable, reliable solutions.

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.

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, 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).

Operate and maintain Tencent's overseas big-data platforms for platform and cluster stability; troubleshoot platform/component errors and performance issues; assist users; optimize workflows, costs, and architectures; and develop operational tools for capacity management, DevOps, automation, coverage analytics, and health monitoring.