Solution Architect (Data Modeler)

Ampstek; St. Louis (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Ampstek is seeking an experienced Solution Architect / Data Modeler to design enterprise data solutions and lead data architecture for large-scale initiatives. The role focuses on conceptual, logical, and physical modeling and architecture leadership.

You will work with stakeholders, enterprise architects, data engineers, and analytics teams to translate requirements into scalable, secure architectures using ETL/ELT pipelines and cloud platforms. Onsite in St. Louis, MO.

Qualifications

• Experience designing enterprise data solutions and architecture.
• Proficiency in conceptual, logical, and physical data modeling.
• Strong knowledge of data integration patterns and ETL/ELT pipelines.
• Familiarity with cloud platforms and data governance practices.

Responsibilities

• Design end-to-end data architecture and solution architectures supporting enterprise business and technology initiatives.
• Develop and maintain Conceptual, Logical, and Physical Data Models.
• Translate business requirements into scalable data models and technical solutions.
• Define enterprise data structures, relationships, entities, attributes, keys, constraints, and data domains.
• Design normalized and dimensional data models, including Star and Snowflake schemas.
• Develop data models for operational systems, data warehouses, data lakes, and analytical platforms.
• Collaborate with Data Engineers to implement physical data models via ETL/ELT pipelines.
• Define data integration patterns between applications, databases, APIs, cloud platforms, and data stores.
• Establish data architecture standards, modeling conventions, and best practices.
• Analyze systems and perform source-to-target data mapping and lineage analysis.
• Identify data-quality issues and opportunities for optimization.

Skills

Data modeling

Enterprise data architecture

Data governance

Data integration

Cloud platforms

Stakeholder collaboration

Tools

SQL

ETL tools

Data modeling tools

Job description

Job Title: Solution Architect (Data Modeler)

Location: St. Louis, MO (Onsite)

JD

Position Summary

We are looking for an experienced Solution Architect / Data Modeler to design enterprise data solutions, develop conceptual, logical, and physical data models, and provide architecture leadership for large-scale data and technology initiatives.

The role will work closely with business stakeholders, enterprise architects, application teams, data engineers, analysts, and technology leadership to translate business requirements into scalable and secure data architecture solutions. The ideal candidate will have strong experience in data modeling, database architecture, data integration, cloud platforms, enterprise data management, and solution architecture.

Key Responsibilities

Design end-to-end data architecture and solution architectures supporting enterprise business and technology initiatives.

Develop and maintain Conceptual, Logical, and Physical Data Models.

Translate business and functional requirements into scalable data models and technical solutions.

Define enterprise data structures, relationships, entities, attributes, keys, constraints, and data domains.

Design normalized and dimensional data models, including Star and Snowflake schemas.

Develop data models for operational systems, data warehouses, data lakes, and analytical platforms.

Collaborate with Data Engineers to ensure physical data models can be efficiently implemented through ETL/ELT pipelines.

Define data integration patterns between applications, databases, APIs, cloud platforms, and enterprise data stores.

Establish data architecture standards, modeling standards, naming conventions, and best practices.

Analyze existing systems and perform source-to-target data mapping and data lineage analysis.

Identify data-quality issues, redundancies, inconsistencies, and opportunities for data-model optimization.

Data Modeling Responsibilities

The Data Modeler portion of the role would typically include:

Conceptual Data Modeling

Logical Data Modeling

Physical Data Modeling

Entity Relationship Diagrams (ERDs)

Metadata management

Source-to-target mapping

Data normalization and denormalization

Dimensional modeling

Master and reference data

Data governance and data quality

Database performance considerations

Data integration and transformation requirements