Top Data Engineer Jobs in Singapore

Lead design, build, test and deploy scalable data-intensive web analytics systems and APIs for national-security use cases. Guide performance, reliability, proofs-of-concept (including agentic AI), mentor juniors, and collaborate with product and stakeholders to deliver impactful solutions.

Manage and optimize a scalable, secure Data Exploration Platform: deploy, patch, tune performance, enforce access controls, support stakeholders with technical guidance, and drive platform improvements and best practices for data analytics and visualizations.

The Senior Data Engineer will design and implement data pipelines, modernize systems, and ensure data governance on cloud platforms. Responsibilities include real-time data processing, ETL workflow creation, and collaboration with teams on data solutions.

Design, develop, and evaluate AI-driven solutions for sustainable living. Collect and preprocess multimodal datasets, build data pipelines and experiments, analyze model performance, iterate on models, and collaborate on publications, reports, and demonstrations.

The role involves managing data ingestion pipelines, collaborating with scientists for data optimization, and ensuring efficient data collection to support AI model training.

Designs and implements a scalable hybrid cloud data platform on GCP to process petabytes of data, support ML workloads, automate infrastructure provisioning (Terraform), operate containers (Kubernetes/Docker), monitor and secure systems, and resolve L2/L3 operational issues using CI/CD and TDD practices.

Design and build generative and agentic AI solutions and end-to-end ML applications. Analyze structured and unstructured data, develop ML models, deploy pipelines, dashboards and cloud architectures. Advise clients, run workshops, support pre-sales PoCs, and translate business requirements into scalable AI solutions while collaborating with stakeholders.

Lead development and deployment of deep learning and probabilistic models for multi-physics engineering problems. Architect scalable data pipelines, collaborate with simulation engineers, drive R&D, mentor junior team members, and work directly with customers to integrate models into production, including onsite customer collaboration and presentations.

Sustain and operate Marvell's Silicon.da test data analytics platform: monitor and resolve data loading and quality issues, support users via ServiceNow/Slack, coordinate with vendors and foundries, maintain documentation, and drive issue closure and operational readiness.

Lead design and implement enterprise data integrations and movement across systems. Define data contracts, map/transform data, support GenAI data preparation, coordinate deliveries, troubleshoot integrations, and document interfaces while working across platform, application, infrastructure, security teams.

Own and operate thousands of mission-critical ETL pipelines, manage and resolve data incidents, validate and remediate data, build automation and tooling to improve reliability, and collaborate with portfolio managers, data scientists, and vendors to ensure accurate, timely data delivery for trading research and execution.

Support construction and maintenance of overseas advertising data reports and indicators. Develop and maintain data flows for the overseas advertising system. Collaborate with cross-functional teams to translate business requirements into data engineering solutions while ensuring data quality, reliability, and performance.

Design and deliver production-ready AI and data science solutions across CRM, retail, marketing and operations. Build ML models, GenAI/RAG pipelines, feature engineering and training workflows; collaborate with data engineering to deploy, monitor and optimize models; run experiments and measure business impact while ensuring responsible AI, security and governance.

Build, operate, and harden a cloud-native data platform on Kubernetes using IaC and GitOps. Provision and run lakehouse, streaming, orchestration, and managed DB services; implement governance, self-service interfaces, SLIs/SLOs, runbooks, incident response, upgrades, and platform security to keep multi-tenant data infrastructure reliable and scalable.

Inspect and profile new financial datasets, build and test conceptual/logical/physical data models, document field definitions and relationships, collaborate with Data Engineering to produce ETL/ELT-ready schemas, and maintain model alignment and data quality.

Develop and maintain high-performance components of a distributed data platform in C++, including storage engines, query/compute execution, RPC/networking, and distributed coordination; collaborate with research and engineering teams, troubleshoot production issues, and participate in code review and productionization.

Own and build a streaming-first data platform for high-volume IoT telemetry: ingestion, medallion modeling (bronze/silver/gold), semantic metrics layer, data quality, observability, database architecture, self-serve analytics, and LLM-friendly tooling. Drive roadmap, dashboards, and rapid operational insights across a global robot fleet.

Lead design and maintenance of scalable data pipelines, ETL/ELT processes, and data warehouse architectures. Optimize infrastructure and data quality, build automated monitoring, support high-priority analyses, and partner with Product, Engineering, and GTM teams to deliver actionable data solutions.

The Data Analytics Developer role requires experience in finance, specifically in banking and financial services, strong data analysis skills, and knowledge of PL/SQL.

The Sr Big Data Engineer will develop data pipelines using big data technologies, manage projects, and implement data modeling for analysis while ensuring efficient querying and performance.

Lead site-based CSA activities for data centre projects: manage contractors, ensure compliance with design, safety and quality, coordinate stakeholders, produce reports, and support project leadership.

Design and own large-scale Web3 big data platform and pipelines (real-time and batch), maintain data warehouse/lake, integrate AI/LLM capabilities (RAG, embeddings, Text2SQL), collaborate with AI, product, risk and growth teams, and optimize performance, reliability, and data quality for on-chain analytics and ML use cases.

Operate and support multi-cloud data platform components (Alibaba Cloud and AWS). Build monitoring, SLA/SLOs, respond to incidents, troubleshoot and root-cause issues, optimize compute/storage and cloud costs, develop automation and operational tooling, and collaborate with data and platform teams and cloud vendors to improve platform reliability.

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.

Lead architecture and delivery of post-trade data systems, designing near-real-time and historical data pipelines. Drive technical roadmap, mentor engineers, implement robust testing and monitoring, resolve incidents, and integrate analytics technologies to support trading product features and operational stability.