Top Data Engineer Jobs in Singapore

Manage, maintain, and optimize critical database systems (SQL Server, Oracle). Perform installs, patches, backups, access control, monitoring, performance tuning, incident/change/problem management, on-call support, capacity planning, automation, reporting, and collaborate with global teams and site stakeholders.

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.

The role involves developing AI-driven solutions for semiconductor engineering, focusing on GenAI systems, data pipelines, and machine learning models while collaborating with cross-functional teams.

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.

Lead design and delivery of site-level data architectures, pipelines and analytics to enable real-time decision-making and AI-enabled solutions across manufacturing, quality, engineering and supply chain in a regulated (GMP) environment. Drive digital roadmap execution, governance, adoption, and continuous improvement with cross-functional stakeholders.

Design, build, and ship agentic AI applications that answer business questions over governed data. Implement RAG, NL-to-query, tool/function calling, and evaluation/guardrails. Partner with data teams on metrics, models, pipelines, and apply strong data governance and security for financial services.

Lead design and implementation of cloud DevOps strategy for global Data and AI infrastructure. Build observability and detection capabilities, enable self-service tooling for data teams, manage scalable, secure infrastructure, and align DevOps practices and standards across geographies.

Design, build, and maintain scalable data pipelines, data models, feature stores, and data catalogues to support AI model training and inference. Ensure data quality, lineage, governance, and secure integration of diverse real-time and batch transport data sources. Collaborate with AI architects, infrastructure engineers, and stakeholders to deliver production-grade data products and maintain pipeline reliability.

The Associate Data Engineer will contribute to Machine Learning and NLP projects, collaborate with cross-functional teams, and enhance threat detection through data-driven intel.

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 role involves developing advanced data analytics solutions for yield and quality improvement in HBM product engineering, collaborating with cross-functional teams and driving innovation in AI and machine learning.

Work with cross-functional teams to design, evaluate and deploy NLP and generative-AI systems. Build evaluation frameworks, run experiments, perform error analysis, and help productionize and monitor model improvements while ensuring reproducibility, responsible AI and clear communication to stakeholders.

This role involves designing and maintaining data pipelines, managing APIs, collaborating with teams, and ensuring data quality and governance.

As a Data Engineer, you'll design and develop cloud-native data platforms, create scalable data pipelines, and support analytics and reporting across teams.

The Data Engineer will optimize data infrastructure, develop scalable ETL processes, establish data governance, and collaborate with data teams to ensure data accuracy and performance.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize offline and real-time data pipelines and warehouses for product data. Lead end-to-end R&D for online product data pipelines, ensure timeliness and accuracy, perform Flink-based real-time processing and performance tuning, and enable business growth through data products and services.

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.

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.

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.

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.

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.

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, 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.