Top Data Engineer Jobs in Singapore

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.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

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.

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.

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.

Lead technical pre-sales for enterprise data management: run technical discovery, demos, POCs, solution design, RFP responses, partner enablement, and executive briefings to drive adoption and validate value.

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.

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.

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.

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.