Data Modeler

Our client, a leading global financial services company, has approximately 200 million customer accounts and does business in more than 140 countries. They provide consumers, corporations, governments and institutions with financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. Data ModelerThe data modeler designs, implements and documents data architecture and data modeling solutions, which include the use of relational, dimensional, and NoSQL-Hive databases. These solutions support enterprise information management, business intelligence and other business interests.The successful candidate will:1. Be responsible for the development of the conceptual, logical, and physical data models, the implementation of RDBMS-MS SQL Server, data marts, and data lakes on target platforms (SQL/NoSQL).2. Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices. The candidate must be able to work independently and as part of a team.Responsibilities• Implement business and IT data requirements through new data strategies and designs across all data platforms (relational-MSSQL Server, dimensional, and NoSQL-Hive) and data tools (reporting, visualization, analytics).• Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models• Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.• Hands-on modeling, design, configuration, installation, performance tuning, and sandbox POC.• Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.• Understand and translate business needs into data models supporting long-term solutions.• Create logical and physical data models using best practices to ensure high data quality and reduced redundancy.• Recommend opportunities for reuse of data models in new environments.• Analyze data-related system integration challenges and propose appropriate solutions.• Review modifications to existing scripts to improve efficiency and performance.• Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.