Data Architect: Design Enterprise Data Models & Flows

EXL Talent Acquisition Team; Chicago (IL); On-site; USD 120,000 - 150,000; Full time

Job summary

EXL Talent Acquisition Team in Chicago seeks a Data Architect to provide technical leadership in the analysis, design, development, rollout and maintenance of enterprise data models and solutions.

You will translate business needs into data models, collaborate with the Application Development team to implement data strategies and data flows, create and maintain conceptual, logical and physical models with metadata, and drive best practices for naming and versioned documentation.

Responsibilities

• Understand and translate business needs into data models and architecture supporting long-term solutions.
• Work with the Application Development team to implement data strategies, build data flows and development of data models.
• Create and maintain conceptual, logical, and physical data models using best practices to ensure high data quality and reduced redundancy, along with corresponding metadata.
• Optimise and update logical and physical data models to support new and existing projects.
• Develop best practices for standard naming conventions and coding practices to ensure consistency of data models.
• Organized and rigorous with versioning of documentation and models to ensure consistent delivery and understanding between the client and development team.
• Recommend opportunities for reuse of data models in new environments.
• Perform reverse engineering of physical data models from databases and SQL scripts.
• Evaluate data models and physical databases for variances and discrepancies.

Job description

EXL Talent Acquisition Team in Chicago seeks a Data Architect to provide technical leadership in the analysis, design, development, rollout and maintenance of enterprise data models and solutions.

You will translate business needs into data models, collaborate with the Application Development team to implement data strategies and data flows, create and maintain conceptual, logical and physical models with metadata, and drive best practices for naming and versioned documentation.