Senior Data Modeler

Required Education: 
4 years College Degree or equivalent technical study

Required Skills:
• Experience in Data Modeling for Databases like DB2, AWS’ Redshift, Dynamo DB, Postgres, SQL Server (8 Years)
• Data modeling software (6 Years)
• AWS and Other Cloud Systems (3 Years)
• Strong SQL and data profiling skills in various relational databases (8 Years)
• Analyzing source databases, source data, source database referential integrity, and profiling source data (8 Years)
• Experience in building Data Lakes for both structured and unstructured data (2 Years)

Preferred Skills:
• Database tuning techniques (4 Years)
• Exposure to Snowflake’s Cloud Data Warehouse

This Senior Data Modeler position is for designing and implementing modern data modeling solutions using relational, dimensional, snowflake, star, and data lake that meet client needs using conceptual, logical, and physical data models. The Senior Data Modeler should be capable of modelling for various databases like AWS Redshift, S3, DynamoDB, DB2, and PostgreSQL. 

Job Duties:
• Responsible for the design, development, and implementation of Data Warehouse database structures and Analytical Database structures
• Work with Business groups and Application Teams during pre- & post-assessment periods
• Write complex SQL’s, write and execute Stored Procedures for the analysis of data
• Understand the needs of the Business team and BI team for building a proper Data Warehouse and DataMart
• Develop structured and unstructured data models for BI and Analytics
• Understand and translate business needs into data models supporting long-term solutions for different client systems
• Work with the Application Development teams (ex, Pega Applications) to implement data strategies, build data flows, and develop conceptual data models
• Create logical and physical data models using best practices to ensure high data quality and reduced redundancy
• Optimize and update logical and physical data models to support the Data Initiative project for different phases of implementation
• Develop best practices for standard naming conventions and coding practices to ensure consistency of data models
• Recommend opportunities for reuse of data models in a new environment
• Perform reverse engineering of physical data models from databases like DB2 and SQL scripts
• Evaluate data models and physical databases for variances and discrepancies across the different source systems
• Analyze data-related system integration challenges and propose appropriate solutions, and develop the model according to the client's Standards
• Work with all IT teams, like System Analysts, Engineers, Programmers, BI, and others, on project limitations and capabilities, performance requirements, and interfaces
• Review modifications to existing software to improve efficiency and performance