Data Modeler

Data Modeler Responsibilities & Duties

Be responsible for the development of the conceptual, logical, and physical data models, the implementation of RDBMS, operational data store (ODS), data marts, and data lakes on target platforms (SQL/NoSQL)

Design and implement data models to meet the organizational needs.

Develop conceptual, logical, and physical data models.

Collaborate with business analysts and stakeholders to gather and analyze requirements.

Ensure data integrity, stability, and security across databases.

Establish and enforce data modeling standards and best practices.

Create and maintain metadata management and data governance.

Optimize and tune data models for performance.

Conduct data model reviews and provide feedback for improvement.

Document and communicate data models and their specifications.

Use data modeling tools and software for model creation and management.

Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices.

Data Modeler Qualifications & Skills

Experience with data modeling tools such as ER/Studio, ERwin, or PowerDesigner.

Implementing ETL pipelines within and outside of a data warehouse using Python and Snowflakes Snow SQL

Querying Snowflake using SQL.

Development of scripts using Unix, Python, etc. for loading, extracting, and transforming data.

Strong knowledge of SQL and database management systems like Oracle, SQL Server, or MySQL.

Experience with big data technologies like Hadoop or Spark.

Familiarity with cloud-based data services such as AWS or Azure.

Background in data governance and data quality concepts.

Experience in Agile or Scrum environments.

Bachelor's degree in Computer Science, Information Technology, or a related field.

Proficiency in data modeling techniques and methodologies.

Strong understanding of database principles and data modeling standards.

Experience in designing and implementing complex data models.

Knowledge of ETL processes and data integration.

Attention to detail and commitment to data accuracy.

Ability to translate business requirements into data models.

Capability to work independently and in a team environment.