Top Data Engineer Jobs in Singapore

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.

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.

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.

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 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.

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.