Company
DataModeler
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Modeler. Skills: Data modeling, Cloud data platforms, AI/ML enablement. Design data models. Maintain data models”
Industry & Context.
Problem-solving skills
What They're Looking For.
Must Have
5+ years experience in data modeling, 3+ years supporting enterprise-scale data platforms, Experience modeling data for analytics, Experience modeling data for reporting, Experience modeling data for AI use cases, Bachelor's degree in Computer Science, Bachelor's degree in Information Systems, Bachelor's degree in Data Management, Bachelor's degree in Engineering, Bachelor's degree in a related field, Expertise in data modeling concepts
Nice to Have
Master's degree is a plus, Experience in regulated industries, Familiarity with data governance tools, Familiarity with metadata tools, Familiarity with lineage tools, Experience with large data ecosystems, Experience with multi-domain modeling, Exposure to real-time architectures, Exposure to event-driven architectures
What You'll Do.
Develop dimensional models
Develop relational models
Develop hybrid models
Translate business requirements
Ensure models support batch use cases
Ensure models support near-real-time use cases
Design models optimized for Snowflake
Partner with data engineering teams
Implement models in Databricks
Support cloud data storage solutions
Ensure models align with analytics consumption
Ensure models align with BI consumption
Collaborate with data engineers
Ensure data pipelines populate models
Ensure data pipelines maintain models
Define source-to-target mappings
Define transformation logic
Ensure consistency of data definitions
Design data models for ML workloads
Design feature models for ML workloads
Design data models for GenAI workloads
Design feature models for GenAI workloads
Partner with data scientists
Ensure feature usability
Ensure feature consistency
Ensure feature lineage
Enable explainability for AI initiatives
Enable traceability for AI initiatives
Enable reuse of data assets
Align models with business glossaries
Align models with metadata standards
Align models with lineage standards
Align models with data quality rules
Align models with validation checks
Ensure models reflect data ownership
Ensure models reflect domain boundaries
Ensure models reflect stewardship responsibilities
Maintain documentation for data models
Maintain documentation for definitions
Maintain documentation for relationships
Contribute to modeling standards
Contribute to best practices
Contribute to design guidelines
Support impact analysis for changes
Engage with business users
Engage with product owners
Validate data requirements
Support analytics teams
Support reporting teams
Act as subject matter expert
How You'll Work.
Team & Collaboration
Data engineers; Governance teams; Analytics teams; Business stakeholders; Data scientists
Communication Scope
Technical stakeholders; Non-technical stakeholders
Full Job Description
### **Role Overview** ### We are seeking a Data Modeler to design, develop, and maintain high‑quality conceptual, logical, and physical data models that support analytics, reporting, AI/ML, and GenAI use cases. This role partners closely with data engineers, governance teams, analytics, and business stakeholders to ensure data structures are scalable, performant, governed, and aligned with business semantics across AWS and Azure data platforms. ### ### **Key Responsibilities** ### Data Modeling & Design ### Design and maintain conceptual, logical, and physical data models to support enterprise analytics, reporting, and AI use cases. ### Develop dimensional, relational, and hybrid models (e.g., star, snowflake, data vault where applicable). ### Translate business requirements into well‑structured, reusable data models. ### Ensure data models support both batch and near‑real‑time use cases. ### Cloud Data Platforms & Analytics ### Design data models optimized for Snowflake, including performance, scalability, and cost efficiency. ### Partner with data engineering teams to implement models in Databricks (Spark) environments. ### Support cloud data storage solutions such as S3 and ADLS Gen2. ### Ensure models align with analytics and BI consumption patterns. ### Data Integration & Transformation Alignment ### Collaborate with data engineers to ensure data pipelines correctly populate and maintain models. ### Define source‑to‑target mappings and transformation logic. ### Ensure consistency of data definitions across source systems and downstream consumers. ### AI / ML & Advanced Analytics Enablement ### Design data and feature models that support ML and GenAI workloads using SageMaker and Amazon Bedrock. ### Partner with data scientists to ensure feature usability, consistency, and lineage. ### Enable explainability, traceability, and reuse of data assets for AI initiatives. ### Data Governance & Quality ### Work closely with data governance teams to align models with: #
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