Data Modeler

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:

Business glossaries

Metadata and lineage standards

Data quality rules and validation checks

Ensure models reflect data ownership, domain boundaries, and stewardship responsibilities.

Documentation & Standards

Maintain comprehensive documentation for data models, definitions, and relationships.

Contribute to modeling standards, best practices, and design guidelines.

Support impact analysis for changes to data structures.

Collaboration & Stakeholder Engagement

Engage with business users, analysts, and product owners to validate data requirements.

Support analytics and reporting teams in understanding and using data models effectively.

Act as a subject matter expert for enterprise data structures.

Required Skills & Experience

Technical Skills

• Strong expertise in data modeling concepts (conceptual, logical, physical).

• Proficiency in SQL; familiarity with Python is a plus.

• Hands‑on experience with Snowflake data modeling and performance optimization.

• Experience working with Databricks / Spark‑based data platforms.

• Understanding of cloud data architectures on AWS and/or Azure.

• Familiarity with data integration, ETL/ELT processes, and analytics workloads.

• Understanding of data needs for AI/ML and GenAI platforms (SageMaker, Bedrock).

Soft Skills

• Strong analytical and problem‑solving skills.

• Ability to translate complex business concepts into clear data structures.

• Excellent communication skills with both technical and non‑technical stakeholders.

• Detail‑oriented with a focus on data consistency and usability.

Education Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, or a related field.

Master’s degree is a plus.

Experience Requirements

5+ years of experience in data modeling, analytics engineering, or related roles.

3+ years supporting enterprise‑scale data platforms in cloud environments.

Experience modeling data for analytics, reporting, and AI use cases.

Preferred Qualifications

Experience in regulated industries (e.g., healthcare, life sciences, finance).

Familiarity with data governance, metadata, and lineage tools.

Experience with large, complex data ecosystems and multi‑domain modeling.

Exposure to real‑time or event‑driven architectures.