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Build and optimize SQL-based data pipelines and ETL/ELT workflows using Azure Data Factory, Microsoft Fabric, and cloud technologies for analytics and reporting.
Builds and maintains data pipelines and warehouses, automates data workflows, and ensures accuracy for analytics and reporting using SQL, Python, and APIs.
Build and optimise Azure data pipelines using ADF and Databricks, support migration to Azure Data Lake, and drive data quality and governance in a collaborative environment.
Designs and maintains scalable data pipelines and ETL/ELT processes to integrate sales, CRM, ERP, and telematics data into a data warehouse for analytics and reporting.
Build and maintain scalable data pipelines on GCP, using BigQuery, Airflow, and Python to support marketing analytics and reporting dashboards in Power BI and Looker.
Designs and maintains scalable data pipelines and ETL/ELT processes, optimizes SQL queries, and builds data warehouse structures for a product company.
Designs and builds scalable Snowflake data solutions for analytics and AI, including pipelines, semantic layers, and Cortex-enabled features using SQL, Python, and modern data-warehousing practices.
Designs and maintains SQL/ETL/BI pipelines to feed analytics and reporting for digital products and marketing, while leading AI/ML initiatives including model development, prompt engineering, and MLOps on Azure.
Designs and maintains ELT pipelines with dbt, Fivetran/Airbyte to feed AI and analytics models, ensuring scalable, cost-efficient data infrastructure.
Build and maintain ELT pipelines with dbt, Fivetran/Airbyte, and data warehouses to support analytics and AI workloads.
Designs and leads end-to-end Azure-based data, analytics, and AI solutions, including Power BI, Databricks, and Azure OpenAI, ensuring security, scalability, and governance.
Builds and optimizes data pipelines using PySpark, Azure Databricks, and SQL to process and store data in cloud environments like ADLS Gen2 and Oracle.
Lead a team building scalable data pipelines and warehouses on AWS, enabling self-service analytics with Python, SQL, and tools like Redshift and Airflow.
Design and lead enterprise-scale Data, AI, and Generative AI solutions for Malaysian enterprises and public-sector clients, including cloud-native architectures and MLOps/LLMOps pipelines.
Design and optimize data pipelines to feed fraud-risk analytics and regulatory reports; build ETL/ELT processes, data marts, and dashboards for Risk, Fraud, and Compliance teams.
Build and maintain scalable ETL/ELT pipelines and orchestrate workflows with Airflow on GCP, ensuring data integrity for enterprise clients.
Founded in 2003, iZeno was built on one conviction: enterprises deserve technology that doesn't just keep pace with change - it drives it. As part of Logicalis Asia Pacific - the Architects of Change™ - our team of…
SD Guthrie Berhad is seeking a data engineering professional to help define and execute the department’s data vision. You will design and maintain ETL pipelines from SAP and diverse sources, build scalable analytics…
iZeno is seeking an experienced Lead Data Engineer to design and deliver modern enterprise data platforms. You will provide technical leadership, shape data architecture, and mentor engineers while delivering scalable,…
Build and maintain cloud data pipelines using Azure Data Factory and Databricks to ingest, transform, and expose data for analytics and AI workloads.
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