Our client is seeking a Senior Data Modeler to develop scalable and compliant banking data models on Google Cloud Platform. (2024-46)

Our client is seeking a Senior Data Modeler to develop scalable and compliant banking data models on Google Cloud Platform. (2024-46)

Must Have:

• 10+ years of experience in data modeling and architecture, specifically within banking domains such as retail banking, business banking, risk, and capital markets.
• Expert knowledge of data modeling tools, techniques, and BIAN modeling standards.
• Strong experience with Google Cloud Platform (GCP), particularly BigQuery, for data warehousing and analytics.
• In-depth understanding of data integration, ETL processes, and database technologies such as SQL Server, Oracle, PostgreSQL, and BigQuery.

Nice to Have:

• Hands-on experience in evaluating and selecting database technologies for banking-specific applications.
• Experience with data security, privacy, and governance practices in banking.
• Proficiency in mentoring junior team members in banking data modeling and GCP technologies.
• Familiarity with data governance initiatives and evolving banking regulations.

Description:

The Senior Data Modeler will work with the client’s diverse banking operations to design and implement robust, scalable, and regulatory-compliant data solutions. Using tools like GCP and BigQuery, this role focuses on supporting retail and business banking, risk management, and capital markets. Responsibilities include designing data models, developing data integration processes, adhering to BIAN modeling standards, and ensuring alignment with banking regulations.

Responsibilities:

• Design conceptual, logical, and physical data models for banking domains with BIAN modeling standards.
• Collaborate with stakeholders to translate business requirements into data models for retail banking, business banking, risk, and capital markets.
• Implement scalable data solutions using GCP and BigQuery, ensuring optimal performance and cost-efficiency.
• Develop and maintain ETL processes to integrate data from various sources.
• Ensure compliance with banking regulations and data governance policies, particularly for sensitive data.
• Mentor junior data modelers on data modeling best practices and GCP tools.
• Document data architecture, including BIAN mappings and GCP implementations.
• Monitor and optimize data performance for banking applications on GCP.