CLIENT: Our client is one of the biggest global energy companies awarded as HR Asia Best Companies to Work for in Asia.
JOB DESCRIPTION Lead the design and implementation of data collection, storage, transformation, orchestration (movement), and consumption to achieve optimum value from data. They are the technical leaders within data delivery teams. Play a key role in modeling data for optimal reuse, interoperability, security, and accessibility as well as in the design of efficient ingestion and transformation pipelines. Ensure data accessibility through a performant, cost-effective consumption layer that supports use by citizen developers, data scientists, AI, and application integration. Instill trust through the employment of data quality frameworks and tools. The data architect predominantly works within the Azure Data Analytics Platform, but they are not limited to it. Optimize costs for delivering data and ensure compliance with enterprise standards, contributing to the evolution of those standards resulting from changing technologies and best practices. JOB QUALIFICATION Proven experience as a Data Architect or similar role. Strong knowledge of data modeling, data warehousing, and data integration techniques. Proficiency in database management systems (e.g., SQL Server, Oracle, PostgreSQL). Experience with big data technologies (e.g., Hadoop, Spark) and data lake solutions (e.g., Azure Data Lake, AWS Lake Formation). Familiarity with cloud platforms (e.g., Microsoft Azure, AWS, Google Cloud Platform). Strong understanding of data governance and security best practices. Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills. Bachelor's degree in computer science, Information Technology, or a related field (or equivalent experience). Experience in Erwin, Azure Synapse, Azure Databricks, Azure DevOps, SQL, Power BI, Spark, Python, R. Familiarity with Azure AI/ML Services, Azure Analytics: Event Hub, Azure Stream Analytics, Scripting: Ansible. Experience with machine learning and advanced analytics. Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). Understanding of CI/CD pipelines and automated testing frameworks. ACTIVE DATES
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