Top Data Engineer Jobs in Singapore

Design, build, test, deploy, and maintain scalable, resilient cloud-based data pipelines and microservices (primarily on AWS). Implement ETL/ELT, data lake/warehouse solutions, support analytics integration, enforce data quality, and follow Agile DevOps practices with CI/CD and IaC.

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.

Develop and maintain global Data and AI infrastructure supporting data engineering and analytics teams. Lead DevOps strategy, evolve scalable and secure cloud infrastructure, manage operational platforms, build observability and detection capabilities, and establish engineering standards across international teams. The role includes working with Kubernetes, Terraform, GitOps, and data streaming platforms such as Kafka, Flink, or Spark.

Lead global analytics engineering initiatives for electronic trading and research. Design, build, and optimize real-time data pipelines, scalable APIs, analytics libraries, and production trading tools. Apply AI to data engineering, analytics, and SDLC automation while ensuring reliability, testing, security, and continuous delivery. Collaborate with trading and research teams, onboard datasets, mentor engineers, manage workstreams, and establish technical standards across distributed teams.

Designs and maintains scalable batch and real-time data pipelines, data warehouses, lakes, marts, and APIs. Extracts, cleans, transforms, and integrates data using cloud platforms and big-data frameworks. Develops reliable ETL/ELT solutions, supports backend applications, applies data governance and security practices, and collaborates with architects, analysts, developers, designers, and project managers in an Agile environment.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

The role involves leading data analytics initiatives to improve yield and quality, developing algorithms, enhancing data infrastructure, and collaborating with cross-functional teams to drive innovation in HBM product engineering.

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.

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.

Designs, develops, and optimizes large-scale data processing pipelines using Spark, Scala, Hadoop, Elasticsearch, and Quantexa. Responsibilities include data transformation, integration, performance tuning, data quality, governance, monitoring, documentation, and deployment on OpenShift. The role also supports system integration, coding, environment setup, CI/CD, troubleshooting, and technical solution development across project lifecycles.

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.

Develop and deploy product-disposition workflows and deviation alert systems using statistical tools and ML/AI to improve yield, reliability, and quality. Build algorithms for data mining, pattern recognition, and wafer/die screening. Collaborate with Fab, design, and quality teams, mentor team members, and drive technical innovation to meet KPIs like quality, cost, cycle time, and scale.

Design, build, and operate scalable logging and operational data platforms across on-premise, government cloud, AWS, Azure, and hybrid environments. Responsibilities include log ingestion, routing, enrichment, storage, indexing, search, streaming, data integration, Infrastructure as Code, CI/CD, security, governance, reliability engineering, monitoring, incident response, and operational support. The role also defines schemas, data contracts, retention policies, platform interfaces, and reusable capabilities for downstream engineering, analytics, dashboards, and automation consumers.

Designs and operates observability across on-premises, cloud, hybrid infrastructure, applications, and networks. The role establishes OpenTelemetry instrumentation standards, telemetry collection, dashboards, SLIs, SLOs, alerting, and operational health indicators. Responsibilities include integrating telemetry sources, supporting incident investigation, improving service reliability, managing retention and security requirements, automating deployments with Infrastructure as Code, and maintaining runbooks. The engineer collaborates with application, infrastructure, network, security, platform, and operations teams and participates in on-call support.

Build and operate production-grade data pipelines across on-premises and cloud environments. Responsibilities include data ingestion, ETL/ELT, transformation, modelling, quality validation, lineage, monitoring, secure integration, and reusable analytical data products. The role supports dashboards, reporting, machine learning, and operational analytics while implementing CI/CD, Infrastructure as Code, data governance, reliability controls, incident response, and production support.

Design, implement, and maintain data-driven processes and analytics solutions. Collect, cleanse, automate, and prepare data from disparate sources. Build KPIs and dashboards, apply statistical and machine learning methods, collaborate with stakeholders, perform testing and deployment, and train users to enable data-driven decision making and process improvements.

The role focuses on developing product disposition solutions, enhancing yield and reliability through data analysis, and mentoring team members while collaborating across teams.

The IT Engineer (Data Services) is responsible for database system management, performance tuning, compliance, incident resolution, and automation of database operations.

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.

Design, build, and maintain data models, ETL pipelines, dashboards, and APIs to support industry models, research, and consulting. Ensure data accuracy, completeness, and observability while integrating new datasets and cloud infrastructure. Collaborate with analysts and stakeholders to deliver robust, modular production data systems with minimal supervision.

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

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.