Top Data Engineer Jobs in Singapore

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.

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.

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.

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.

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.

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.

Design, build, and support end-to-end data solutions for Global Markets: gather requirements, source and transform data, develop data models, ETL, dashboards (Tableau/Power BI/Qlik), administer analytics platforms, ensure production reliability, and support advanced analytics and migration from Tableau to Power BI.

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, build, and optimize end-to-end data ingestion and ETL pipelines on AWS into governed data lake zones. Implement data quality frameworks, error handling, and metadata/catalog management. Use IaC (Terraform/AWS CDK), write tests, support UAT, produce runbooks/documentation, and participate in Agile delivery.

Own and maintain the analytics stack: implement tracking from user interaction to Mixpanel/GA4/AppsFlyer via Segment, manage infrastructure with Terraform, audit pipelines and schemas, translate business needs into tracking, and configure troubleshooting for mobile/web attribution, deep-linking, and postbacks.

Own and maintain analytics instrumentation and data pipelines using Segment, ensuring reliable event tracking to Mixpanel, GA4 and AppsFlyer. Use Terraform for version-controlled analytics infrastructure, monitor and audit data quality, maintain schema documentation, translate business needs into tracking, and configure mobile/web attribution and deep-linking.

Design, develop, and maintain scalable ETL pipelines moving data from Oracle Fusion and EBS into enterprise data warehouses, data lakes, and analytics platforms. Perform BAU EBS database administration and support Fusion-related data preparation, cleansing, validation, and data transition activities.

The role involves designing and maintaining data pipelines, collaborating with stakeholders, ensuring compliance with regulations, and mentoring junior members. Requires data engineering and science expertise.

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 pre-sales technical design and architecture for Agentforce and Data Cloud (D360). Design integrations with external agents and hyperscalers, build prototypes and demos, define prompt engineering and agent lifecycle best practices, advise on data modeling and ingestion patterns, and provide technical enablement and documentation to internal teams and customers.

Design, fine-tune, and deploy generative AI/LLM solutions (LoRA/PEFT). Build RAG pipelines, embeddings, vector DBs, knowledge bases, and evaluate hallucination. Advise on guardrails and collaborate across teams.

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.

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

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.

This role involves designing, building, and maintaining data pipelines for compliance analytics, assessing existing data infrastructure, and collaborating with data scientists and engineers to ensure reliable data for compliance purposes.

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.

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.

Assist in developing backend services and APIs for WeChat, build large-scale data pipelines, and contribute to data streaming infrastructure while collaborating with teams.