Top Data Engineer Jobs in Singapore

Designs, builds, and operates secure AWS-native data pipelines and platforms for batch and event-driven workloads. Develops Airflow workflows using Python and SQL, implements tested data transformations, and integrates enterprise, security, and operational data sources. The role embeds data quality, lineage, observability, governance, encryption, access controls, and monitoring. It also contributes to CI/CD, infrastructure as code, documentation, code reviews, incident analysis, and cross-functional solutions supporting risk and regulatory needs.

Operate and maintain critical database systems supporting manufacturing and engineering. Responsibilities include database installation, upgrades, patching, access management, backups, monitoring, performance tuning, incident resolution, disaster recovery, capacity planning, reporting, automation, and business continuity. The role provides on-call support, collaborates with customers on database requirements, improves operational reliability, and evaluates emerging database technologies and AI-assisted tools.

Leads a team developing and productionizing predictive, machine learning, deep learning, and GenAI solutions for semiconductor manufacturing and HBM product engineering. Responsibilities include yield and reliability modeling, large-scale data pipelines, MLOps, model monitoring and drift control, distributed analytics, deployment, and engineering decision support. The role also requires mentoring engineers, setting technical direction, collaborating cross-functionally, and translating model outputs into improvements in quality, cost, cycle time, and manufacturing efficiency.

Design and build scalable AWS data lakehouse platforms, including ETL/ELT pipelines, Apache Iceberg or S3 Tables implementations, data quality frameworks, governance, CI/CD, infrastructure as code, and production deployments. Optimize performance, cost, reliability, and scalability while documenting solutions and communicating architecture to technical and non-technical stakeholders.

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.

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.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

Develop and maintain ETL data pipelines, optimize data flow and delivery, automate manual processes, and design scalable data infrastructure. Gather telemetry data to improve system efficiency and business metrics. Collaborate with cross-functional teams on requirements and data products, using databases, workflow tools, microservices, and APIs. Preferred experience includes Agile development, monitoring, fault-tolerant pipelines, big data technologies, and search engines.

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.

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.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics 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.

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.

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.