Top Data Engineer Jobs in Singapore

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.

Operate and engineer database systems supporting manufacturing and engineering. Responsibilities include installation, upgrades, patching, access management, backups, performance tuning, monitoring, 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 advanced data analytics and AI/ML initiatives for HBM semiconductor products. Develops machine learning, deep learning, anomaly detection, screening, and deviation-monitoring solutions to improve yield, quality, reliability, and engineering efficiency. Strengthens analytics infrastructure and visualization workflows while collaborating across manufacturing, design, technology development, product engineering, and quality teams. Provides technical leadership, mentorship, and innovation guidance for AI-enabled engineering practices.

Build scalable batch and real-time ETL pipelines and trusted customer-data foundations for analytics, machine learning, operational decision-making, and AI applications. Develop data models and products across warehouses and cloud infrastructure, using technologies such as Databricks, dbt, Spark, Kafka, and Flink. Collaborate with globally distributed teams on complex technical work.

Build and own scalable batch and real-time ETL pipelines for Airwallex’s Customer Data Platform. Develop trusted data foundations, warehouses, models, and data products supporting analytics, machine learning, operational decisions, business intelligence, and AI applications. Partner with globally distributed teams to deliver complex technical solutions using modern data infrastructure and streaming technologies.

Design and maintain data warehouses, lakes, marts, virtualization layers, and scalable batch or real-time pipelines. Extract, clean, transform, integrate, and govern data using cloud platforms and ETL tools. Develop backend APIs and database solutions, collaborate with cross-functional Agile teams, and support data-driven products. Responsibilities include web scraping, data modeling, database design, code reviews, pair programming, and production-grade data engineering across AWS, Azure, and Google Cloud environments.

Supports ETL and data pipeline development, monitoring, validation, quality checks, and integration across multiple systems. Automates recurring data processing and reporting using SQL and Python, maintains technical documentation, assists with ad hoc data requests, troubleshoots data issues, and collaborates with engineers, analysts, and business stakeholders.

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, develop, and maintain scalable data pipelines and architectures for analytics and reporting. Perform ETL and streaming data integration, data modeling, governance, quality management, and workflow optimization across cloud and big data environments. Translate business requirements into data solutions, monitor reliability and performance, and collaborate with stakeholders to advance Walmart’s data strategy.

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

Maintain Cloudflare’s global data center network and operational availability across remote colocation facilities. Responsibilities include configuring network infrastructure, coordinating contractors and vendors, managing deployments and decommissions, creating SOPs and operational documentation, improving processes through automation and AI tools, supporting troubleshooting and root-cause analysis, and participating in a 24/7 on-call rotation. The role also involves basic program management, cross-functional collaboration, and limited travel.

Build and maintain scalable data platforms, databases, ETL processes, and data pipelines across relational and NoSQL systems. Integrate data from multiple sources, optimize SQL queries and database performance, perform data quality checks, troubleshoot complex data issues, and support secure, compliant healthcare data processing. The role also includes testing, code reviews, architecture optimization, and collaboration with technical and non-technical stakeholders.

Provision, stage, troubleshoot, repair, and maintain bare-metal servers, network devices, and data center infrastructure. Collaborate with engineering and installation teams to validate builds, improve deployment workflows, and optimize staging processes for a global cloud security network. The role requires expertise in hardware configurations, physical connectivity, Linux/Unix, BIOS, out-of-band management, virtualization, scripting, and build automation. Occasional travel to a Singapore integration center is required.

Designs, develops, and maintains scalable data pipelines, models, schemas, and data infrastructure. Ensures data quality, governance, security, privacy, and regulatory compliance while supporting data warehouses, lakes, analytics, and business operations. Collaborates with architects, data scientists, and stakeholders; monitors and optimizes pipeline performance; evaluates data technologies; participates in technical reviews; and documents engineering processes and solutions.

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.

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.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Designs and owns scalable batch and streaming data pipelines, data models, and infrastructure for business and AI use cases. Partners with engineering and cross-functional teams to deliver reliable solutions, monitoring, governance, and data quality improvements. The role involves distributed data systems, real-time analytics, knowledge retrieval, and AI-ready infrastructure, with work based five days per week in Singapore.

Build and maintain marketing data infrastructure across APAC by integrating GA4, AppsFlyer, Amplitude, and Braze with GCP. Develop ETL/ELT pipelines, support attribution and measurement modeling, maintain tracking and tagging, enforce data governance, resolve data quality issues, and document pipelines and schemas. Partner with marketing, analytics, and engineering stakeholders to deliver integrations and enable reliable self-service reporting across Singapore, Australia, Japan, and emerging markets.

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.

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.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Lead enterprise banking data platform initiatives, architect data lakes and warehouses, design models using FSLDM, FIBO, and BIAN, and govern data quality, security, lineage, and compliance. Mentor data engineering teams, oversee platform performance and costs, advise financial-services clients on modernization strategies, and establish engineering best practices across cloud and enterprise data platforms.

Build and maintain scalable data engineering infrastructure for scientific, sensor, and operational data. Responsibilities include feature engineering pipelines, data quality monitoring, versioning, lineage, drift detection, data contracts, governance, sensor ingestion, synthetic data pipelines, and MLOps data layers. The role requires strong Python, SQL, pipeline design, and data lifecycle fundamentals, with integration into AWS-based AI platforms and progressive ownership of real-time systems.

Build and scale a financial data platform for investment teams. Responsibilities include evaluating structured and unstructured datasets, developing reliable ETL workflows, improving data infrastructure, and creating reusable tools for data analysis and quality checks. The role works closely with portfolio managers and quantitative researchers and takes projects from requirements through delivery.