Top Data Engineer Jobs in Singapore

Build and maintain scalable enterprise data platforms, pipelines, integrations, and reusable data products for global manufacturing operations. Implement data governance, quality monitoring, metadata, lineage, and master data practices. Develop cloud-native, batch, streaming, and event-driven solutions supporting analytics, AI, reporting, smart factories, and digital twins. Collaborate with architects, engineers, data scientists, and operations stakeholders while applying DevOps, observability, and CI/CD practices.

Build and operate ingestion and ELT pipelines, maintain metadata repositories and internal data tools, implement data-quality checks, reconcile financial data, investigate anomalies, and support analysts. The role includes backend service development, production ownership, AWS-based platform work, and integrating LLM/RAG capabilities into data tools.

Designs, builds, deploys, and operates scalable cloud data lakehouse platforms and production ETL/ELT pipelines. Responsibilities include data architecture, open table formats, automated data quality, governance, access controls, CI/CD, infrastructure as code, analytics and AI enablement, documentation, monitoring, incident response, and cost optimization. The role collaborates with technical and business stakeholders across Databricks, Spark, AWS, and Microsoft Fabric environments.

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.

Build and maintain scalable batch and real-time data pipelines for information security and digital risk analytics. Translate business and risk requirements into technical specifications, data models, ETL solutions, and production-ready data products. Support testing, deployment, monitoring, documentation, and production issue resolution while applying data governance, access control, auditability, and secure-by-design practices. Collaborate with business, risk, technology, and platform stakeholders in a regulated banking environment.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Design and build scalable data infrastructure, backend services, data discovery and lineage systems, and fault-tolerant pipelines processing petabytes of data. Lead system design, code reviews, and technical contributions while developing cloud-based ETL solutions. Collaborate with machine learning and autonomy teams to improve data access, processing, and platform adoption.

Design, implement, and manage large-scale Big Data infrastructure, including Hadoop, Spark, data lakes, storage systems, clusters, and Kubernetes environments. Optimize performance, scheduling, scalability, security, governance, and availability. Support data engineers and scientists, manage capacity planning and infrastructure deployments, and lead engineering teams. The role also involves building streaming and batch pipelines, supporting search workloads, and deploying AI/ML infrastructure.

Design and build scalable onchain data infrastructure, including multi-chain ingestion, real-time and batch processing, data governance, search, storage, and APIs. Develop high-availability systems supporting blockchain explorers, wallets, DEXs, risk controls, token scoring, and related products. Drive technical decisions, cross-functional initiatives, reliability improvements, and AI-enabled engineering capabilities.

Lead advanced data analytics and ML initiatives to improve yield, quality, and reliability for HBM products. Develop ML/DL models, build data infrastructure, integrate solutions into disposition workflows, mentor team members, and collaborate cross-functionally to optimize KPIs including quality, cost, cycle time, and scale.

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.

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.

Implements software development lifecycle tasks under guidance from experienced staff, follows established standards and procedures, makes technical decisions, collaborates with peers and project leads, and contributes to code and design reviews.

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

Own production data platform reliability, monitoring, observability, incident response, data quality controls, and operational improvements. Build automated monitoring and validation frameworks for Databricks and Airflow environments, define SLAs and SLOs, conduct root cause analyses, maintain runbooks, and support production data products. Partner with engineering, platform, governance, and business teams to improve resilience and supportability while mentoring junior engineers.

Supports humanities, arts, and social science researchers through coding, digital methods, data collection and analysis, cloud services, and web or mobile application development. Responsibilities include training researchers, maintaining the Centre website and portal, supporting technical research proposals and publications, managing software development guidelines, and improving research services. The role also involves ethical data scraping, dataset preparation, academic events, and staying current with relevant software development practices.

Provides technical and methodological support for digital humanities, arts, and social sciences research. Responsibilities include coding, dataset collection and preparation, web and mobile application development, cloud service support, website maintenance, digital methods training, research proposal assistance, software development standards, and service improvement. The role also supports academic events and collaborates with researchers and technical teams across NTU and Singapore.

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

Design, build, and operationalise large-scale cloud data pipelines (batch and streaming) using Databricks/Snowflake and cloud platforms to support reporting, automated systems and AI/ML teams. Administer cloud data platforms, optimise SQL/storage, implement CI/CD, IaC, containerisation and serverless solutions, and collaborate with clients, partners and internal teams to scale data engineering deployments.

Design and maintain high-throughput pipelines for large-scale unstructured data processing. Build transformation, enrichment, vector embedding, hybrid retrieval, multimedia analysis, and computer vision capabilities. Ensure data quality, reliability, testing, observability, and performance while collaborating with data scientists, frontend engineers, and product managers to support national security analytics.

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.

Build and operate governed, audit-ready data foundations for client analytics and AI solutions. Responsibilities include designing ingestion pipelines, lakehouse and warehouse architectures, transformation and semantic layers, data quality, master data, lineage, privacy, residency controls, and AI-ready datasets. The role partners with AI engineers, data scientists, and client stakeholders, contributes reusable engineering accelerators, owns engagement-scale data architecture, and mentors junior resources. It is a hybrid Singapore-based Manager-level position with occasional APAC travel.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform solutions, build scalable multi-region pipelines, enforce governance and security, mentor the team, advocate best practices, and influence technical roadmaps to enable AI/ML-driven business impact.