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Design and implement scalable cloud data architectures, lead Data Lakehouse development, and build ETL/ELT pipelines using Python, SQL, and Spark for a government-linked AI platform.
Build and maintain cloud-native data pipelines and Snowflake-based data warehouse for a fintech client, using ELT and dimensional modeling to deliver analytics-ready data.
Architect and maintain scalable data pipelines and warehouses (Snowflake, Redshift, Athena) to power analytics and AI workflows, using Python, SQL, Airflow, and Kafka.
Build and optimize scalable data pipelines on Databricks using Apache Spark, collaborating with data scientists to implement ETL/ELT processes and ensure data quality.
Design and maintain cloud-based data pipelines and analytics platforms on Microsoft Azure, integrating AI/ML workflows and ensuring data quality and governance.
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.
Designs and leads a cloud-based lakehouse platform for Singapore’s public housing authority, building scalable data pipelines and ensuring governance for AI-driven policy and service improvements.
Designs and maintains ETL workflows and data pipelines using Informatica PowerCenter, Python, Spark, and SQL for financial data processing.
Designs and maintains scalable data pipelines and warehouses using SQL, Spark, and ETL tools to power analytics and reporting for business insights.
Design and build Singapore’s government-wide data infrastructure, including Databricks-based Lakehouse pipelines and governance tooling, to securely share and derive value from public-sector data.
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.
Lead a team to design and build scalable data pipelines and enterprise data platforms using Databricks, Spark, and cloud services.
Design and maintain scalable data pipelines, ETL/ELT processes, and cloud-based data warehouses using Spark, SQL, and cloud platforms like AWS/Azure/GCP.
Build and optimize scalable data pipelines on Databricks using Spark and Delta Lake, implementing ETL/ELT processes and ensuring data quality for analytics and compliance.
Designs and maintains ETL workflows with Informatica PowerCenter and Python, builds Spark/Hive pipelines on HDFS, and optimizes SQL for Oracle/Teradata to support banking data platforms.
Build and optimize AWS-based data pipelines and Redshift warehouses for a government project, using Python, SQL, and DevOps practices to ensure secure, scalable data infrastructure.
Designs and optimizes ETL/ELT pipelines and data warehouses using Teradata, Hadoop, Spark, and Informatica for enterprise-scale data processing.
Design and maintain data pipelines and warehouses to power analytics and AI for Singapore’s public sector, using cloud platforms and distributed systems.
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