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Platform Engineer supporting a quantitative trading and research platform in Singapore, working across cloud infrastructure, data pipelines, CI/CD, and internal APIs/tools for risk monitoring and operational workflows.
ARGYLL SCOTT CONSULTING PTE. LTD. seeks a mid-level Application Engineer to design, develop and maintain data pipelines and integration solutions for a Client Reporting / Investment Technology project. You will provide…
Design, develop, and support scalable software solutions and APIs for SGX Group's enterprise data ecosystem, applying cloud-native practices, automation, and AI-assisted development tools alongside Data Engineers, Platform Engineers, and Architects.
Designs, develops, and supports enterprise data platforms and cloud-native architectures, building scalable data pipelines, data warehouses, and analytics-ready datasets while applying NLP and ML to extract value from diverse data sources for enterprise clients.
The Cloud Data Engineer will design and operate scalable data pipelines on AWS using PySpark, Python, and modern orchestration tools. The role focuses on building ETL/ELT workflows, serverless solutions, and implementing IaC practices for data lake architectures.
The Data Engineer will design and optimize big data solutions by building scalable pipelines using Hadoop, Spark, Scala, and Elasticsearch. The role involves working with Quantexa and OpenShift to deliver reliable, governance-driven data platforms.
Senior Data Engineer at Illumina designing unified data frameworks, building scalable pipelines, and delivering data models while partnering with data scientists to explore AI-driven workflow improvements on large structured and unstructured datasets.
The Global Digital Manufacturing Architect & Data Engineer will design and deploy end-to-end software and analytics solutions across global manufacturing sites. The role involves managing data pipelines, web applications, and dashboards using technologies like Python, Java, C#, SQL, Spark, Power BI, and Grafana.
Hands-on role managing data platform operations and designing metadata-driven ELT/ETL frameworks (ingestion, orchestration, quality, monitoring) at NUS. Involves data lakes, data warehouses, and tools like Apache Spark, Iceberg, Trino, Python, and proprietary platforms.
Lead Data Engineer designing and building scalable data platforms and pipelines in Singapore, combining hands-on engineering with technical leadership, mentoring, and architecture decisions while collaborating with analytics, software, and AI/ML teams.
Design and lead cloud-native data platform and Data Lakehouse initiatives for a government housing agency, building scalable data architectures, ETL/ELT pipelines, and multi-cloud migration strategies.
The Lead Data Engineer builds and maintains custom data ingestion and MLOps platforms using distributed systems, Apache Airflow, Kafka, Spark, and Databricks. The role involves designing scalable data pipelines and supporting data scientists in deploying ML models on cloud and on-premises infrastructure.
The Data Engineer will design, develop, and maintain scalable data pipelines using Databricks and cloud platforms to support analytics and machine learning. The role focuses on implementing ETL processes, streaming and batch data integration, and ensuring data quality and reliability.
The Senior Data Engineer will design and manage end-to-end data pipelines for a government-focused platform. The role involves building scalable backends and collaborating on data architecture and governance within a hybrid work environment.
The Data Engineer will design, build, and maintain reliable data ingestion and transformation pipelines using AWS and Databricks. The role involves developing data processing layers, ensuring data quality, and collaborating with cross-functional teams to deliver production-ready data solutions.
The Senior Data Engineer will design and maintain scalable cloud-native data pipelines and Data Lakehouse architectures to support evidence-based decision-making. The role involves leveraging modern data engineering tools like Python, SQL, Spark, and Kafka while implementing infrastructure as code and MLOps practices.
This role involves designing and implementing banking data pipelines, processing data from various sources, and writing SQL for reporting. The position requires expertise in Python, Spark, Teradata, and Hadoop within a data engineering context.
The Lead Data Engineer builds and maintains custom data ingestion and MLOps platforms using open-source technologies like Apache Airflow, Kafka, and Spark. The role involves designing scalable data pipelines on Databricks and supporting data scientists in deploying machine learning models at scale.
Design and implement scalable ETL pipelines and data connectors on Azure Databricks, ensuring data quality and smooth deployment using SQL, relational database design, and Delta Lake.
The Lead Data Engineer will design and build scalable data platforms and pipelines to support analytics, digital products, and AI/ML workloads. This hands-on leadership role involves mentoring engineers, establishing engineering standards, and leveraging AI-enabled tools to optimize data workflows.
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