Data Technologist at Public Investment Corporation

Requirements:

• 5 - 8 years Information Technology experience in data operations roles in data engineering, analytics platforms, data management, or other analytics. 
• At least 3 years of experience must be in a financial services institution, with knowledge of asset management industry, including financial instruments and market data. 
• Experience with MS Power Platform development and Power BI or other Data Visualization tools like QlikSense, Tableau, Business Objectives. 
• Hands-on experience with AI/ML tools like Azure ML, Azure AI, AI Foundry, Copilot Studio, Databricks. 
• Knowledge of statistical programming languages like R, Python, Scala and data query languages like T-SQL, KQL, DA. 
• Experience with data governance tools (e.g., Azure Purview, Axon, Collibra, Alation). 
• Project management, Agile methodology and development life cycle experience. 
• Excellent communication skills, ability to describe findings to a technical and non-technical audience. 
• Strong knowledge of Data Management Body of Knowledge (DMBOK) framework principles. 
• ITIL Foundation and COBIT certified. 
• Data Professional Certificate such as Microsoft Certified: Fabric Analytics Engineer, Microsoft Certified: Azure Data Engineer Associate  

Duties:

• Configure and manage data platforms and services such as Microsoft Fabric, Azure Synapse, Purview. 
• Coordinate integrations between data platforms, data sources and analytical tools and ensure interoperability between systems and orchestration layers. 
• Design and implement robust ETL/ELT workflows and management of the Lakehouse. 
• Design, implement and manage data pipelines including RESTful APIs for data ingestion. 
• Creating semantic models and ensuring data quality and governance within the Fabric environment. 
• Support implementation of data governance policies including metadata cataloging, data lineage, and integration through technical configurations. 
• Develop and maintain controls on data quality, interoperability and sources to effectively manage corporate risk. 
• Assist with the design, implementation and maintenance of the digital architecture, as well as a road map for future development that supports business needs. 
• Leveraging AI/ML to develop, optimize and deploy models to solve business challenges. 
• Carrying out pre-processing of structured and unstructured data. 
• Enhancing data collection procedures to include all relevant information for developing analytic systems. 
• Troubleshoot and resolve data pipeline failures and platform incidents. 
• Ongoing collaboration with Business and IT teams.