Top Data Engineer Jobs in Singapore

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.

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.

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.

Design, build, optimize, and maintain scalable data pipelines for mission-critical national security analytics systems. Responsibilities include processing large datasets, ensuring data quality and governance, implementing testing and monitoring, troubleshooting complex pipeline issues, and experimenting with agentic AI systems and LLM workflows. The role also involves collaborating with product and engineering stakeholders to deliver reliable data products.

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, 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.

Develop and support batch and streaming data pipelines, data models, ingestion workflows, orchestration, cloud infrastructure, and backend services for banking data platforms. The role also supports AI knowledge bases, vector database pipelines, embeddings, document processing, RAG workflows, and semantic search. Responsibilities include monitoring data quality, troubleshooting pipeline issues, participating in testing and code reviews, and collaborating with engineering, AI, and business teams.

Lead application and systems design, development, and deployment; collaborate with stakeholders to translate business needs into technical solutions; manage and mentor developers; ensure technology standards compliance; and troubleshoot technical issues. The role requires strong application development expertise, leadership, communication, strategic thinking, and customer focus within a banking technology environment.

Undergraduate data engineering interns will manipulate high-volume streaming data, design databases, conduct technology scans and proof-of-concepts, acquire and query data, develop and test systems, and present solutions. Potential projects include DPI acceleration using Snort and Regex, PCAP search, and KSQL real-time stream processing.

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.

Provision, stage, troubleshoot, repair, and maintain bare-metal servers, network devices, and data center infrastructure supporting a global cloud security network. Collaborate with network, platform, installation, and maintenance teams to validate deployments and improve workflows. Translate cloud workloads into hardware requirements, diagnose physical connectivity and platform issues, and visit the Singapore integration center as needed. The role requires strong Linux/Unix, server, network hardware, BIOS, and out-of-band management experience, with scripting and automation skills preferred.

Build and maintain batch and streaming data pipelines, data models, ingestion workflows, Airflow orchestration, cloud infrastructure, CI/CD, and backend data services. Support CDC, messaging, real-time processing, vector database pipelines, embeddings, semantic search, and RAG knowledge bases. Collaborate with senior engineers, AI teams, and business stakeholders to deliver reliable datasets and services for banking use cases including risk, fraud, customer engagement, and automation.

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.

Designs and maintains Databricks-based ETL/ELT pipelines on AWS, develops optimized data models for analytics and BI, and supports production GenAI integrations. The role collaborates with data scientists, AI engineers, and business teams to operationalize models, manage data dependencies, implement monitoring and quality standards, and ensure secure, compliant data workflows.

Build and maintain scalable ETL pipelines integrating structured and unstructured data. Automate data workflows, ensure efficient data movement, collaborate with analysts, scientists, and stakeholders, translate business needs into technical solutions, and maintain documentation and data governance compliance for secure, accessible data.

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.

Leads technical sales engagements for strategic APAC professional services customers. Designs modern data platforms and AI-ready architectures using Microsoft Fabric, Azure Databricks, Azure databases, Purview, Azure AI, RAG, semantic search, and governed analytics. Influences enterprise architecture decisions, leads proofs of concept and workshops, resolves technical blockers, develops reusable industry solutions, and connects Microsoft Data + AI capabilities to secure production workflows and professional services outcomes.

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.

Build AI and agent-based solutions, ML models, full-stack React and Python applications, and production-grade AI platform services. Develop MLOps and CI/CD pipelines across GCP and on-premises Kubernetes, deploy model-serving infrastructure, implement observability and security controls, and create reliable agent guardrails, evaluations, and audit trails. Collaborate with enterprise SMEs, contribute to architecture standards, communicate technical recommendations, and own solutions from design through support.

Designs, engineers, secures, and operates enterprise-scale data, analytics, AI, and agentic platforms on GCP. Leads cloud architecture, infrastructure automation, IAM and governance, Kubernetes operations, CI/CD, DevSecOps, production troubleshooting, and reliability improvements. Acts as an escalation point for complex issues, establishes engineering standards, leads design reviews, mentors engineers, and evaluates emerging cloud and AI technologies.

Lead architecture, reliability, scalability, modernization, and operational excellence for enterprise data and visualization platforms. Responsibilities include building Big Data infrastructure, managing Trino, Superset, Tableau, and related tools, migrating legacy applications to cloud-native environments, improving CI/CD, implementing observability with ELK, Prometheus, and Grafana, optimizing capacity and costs, overseeing production support, and mentoring SRE and DevOps teams in a regulated banking environment.

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.

Leads design and development of scalable cloud-based data and analysis platforms. Responsibilities include architecture, implementation, testing, deployment, troubleshooting, documentation, unit testing, and Agile/Scrum collaboration. The role requires full-stack development expertise, strong Java skills, frontend framework experience, distributed databases, REST services, and cross-functional communication. Kubernetes, cloud infrastructure, and CI/CD experience are preferred.

Build and maintain ML-ready pipelines for experimental engineering, sensor, and operational data. Responsibilities include feature engineering, data quality monitoring, dataset versioning and lineage, drift detection, data contracts, governance, sensor streaming ingestion, synthetic data infrastructure, and MLOps data-layer components such as feature stores and training dataset registries. The role requires strong Python, SQL, pipeline design, and foundational understanding of ML data lifecycles.