Data Modeler (Financial Services)

at  Robert Half

Financial Services firm looking for a Data Modelling specialist with Power BI experience.
Financial Services company, who is seeking a talented and experienced Data Modeler to join our team. The ideal candidate will have a strong background in data modeling, with expertise in tools such as Power BI, DBT (Data Build Tool), and Airflow. As a Data Modeler, you will play a crucial role in designing and implementing data models to support our business objectives, ensuring data integrity, efficiency, and accessibility.

REQUIREMENTS:

• Bachelor’s degree in Computer Science, Information Systems, Finance, or a related field. Advanced degree or relevant certifications preferred.
• Experience in data modeling, database design, and development, preferably in the financial services industry.
• Proficiency in Power BI and DAX, with a strong understanding of data visualization best practices and experience in creating complex reports and dashboards.
• Hands-on experience with DBT (Data Build Tool) and Airflow for data pipeline management and orchestration.
• Solid SQL skills and familiarity with relational and non-relational databases (e.g., SQL Server, PostgreSQL, MongoDB).
• Strong analytical and problem-solving skills, with attention to detail and a focus on delivering high-quality solutions.
• Excellent communication and collaboration skills, with the ability to work effectively in a team environment and interact with stakeholders at all levels of the organization.
• Knowledge of financial services products, processes, and regulations is a plus.
  Apply today!
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1- Data Modeling: Design, develop, and maintain robust data models tailored to the needs of our financial services operations. This includes statistical, conceptual, logical, and physical data models.
2- Tool Proficiency: Utilize tools such as Power BI, DAX DBT, and Airflow to design, implement, and manage data pipelines, transformations, and workflows.
3- Performance Optimisation: Optimise data models, ETL processes and queries for performance and scalability, ensuring efficient data processing and analysis.
4- Documentation: Document data models, processes, and workflows to facilitate knowledge sharing and ensure transparency.
5- Collaboration: Work closely with cross-functional teams including data engineers, analysts, and business stakeholders to understand requirements, refine data models, and deliver actionable insights.