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Design and deliver AI-ready data platforms, ML pipelines, and GenAI-specific data flows for clients using cloud ecosystems like AWS, Azure, and GCP.
Design and optimize AI data pipelines, feature stores, and real-time inference systems using Python, Spark, and cloud infrastructure to power machine learning models.
Design and build scalable cloud-native data pipelines and lakehouse architectures for a government housing agency, using Python, Spark, Kafka, and AWS services.
Design and build scalable on-premise data pipelines and lakehouse solutions using Python, PySpark, and SQL Server to power data-driven decisions across a global banking group.
Senior Data Engineer builds and maintains on-premise data pipelines and lakehouse solutions using Python, PySpark, SQL Server, and Kubernetes to enable data-driven decisions across a global banking group.
Lead a team of data engineers to build and scale a modern Data Lakehouse (AWS S3, Iceberg, EMR) and transformation frameworks (dbt) for a global streaming platform, enabling self-serve analytics and AI readiness.
Lead a cloud-based data engineering team to build and maintain robust data pipelines, migrate legacy systems to Snowflake and AWS Glue, and ensure high-quality data flows for analytics and reporting.
Design and maintain scalable data pipelines for a cybersecurity firm, using tools like Apache Airflow and optimizing storage solutions while collaborating with cross-functional teams.
Lead a small team to design, build, and scale data pipelines and platforms using Python, SQL, and cloud tools, ensuring reliable data for analytics and ML products.
Design and maintain scalable data pipelines, ETL/ELT processes, and cloud-based data warehouses using Spark, SQL, and cloud platforms like AWS/Azure/GCP.
Designs and maintains scalable data pipelines and warehouses for a global game publisher, ensuring clean, reliable datasets for analytics and product decisions.
Builds and maintains CDC pipelines to sync Singapore’s legal data into a Neo4j knowledge graph, ensuring AI models stay current with statutory changes via automated ETL and validation.
Lead data engineering projects to build and maintain investment data platforms using Python, PySpark, AWS, and Databricks, enabling analytics and AI-driven insights for a sovereign wealth fund.
Design and build scalable cloud-native data platforms for a major bank, using Lakehouse architectures, real-time streaming, and AWS to deliver analytics-ready datasets.
Build and optimize AI-powered data pipelines for Razer’s gaming products, including feature stores, vector databases, and real-time streaming solutions using Python, Spark, and cloud platforms.
Designs and builds secure, scalable data pipelines and cloud infrastructure for a public-sector agency using Python, SQL, and modern DevOps tools.
Leads AI data engineering at a Singapore government agency, designing scalable data pipelines and governance for AI systems that support national security.
Build and maintain scalable data models and ETL pipelines for a global fintech platform, ensuring clean, reliable data for analytics and AI use cases.
Build and secure data infrastructure for AI-driven semiconductor manufacturing, including ETL pipelines, data lakes, and governance for LLM applications.
Build AI-ready data pipelines and knowledge systems for an investment firm’s agentic AI, integrating structured financial data, unstructured research, and real-time feeds into vector stores, graph databases, and retrieval pipelines.
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