Senior Data Modeler

Data Modeling and Design

• Design and maintain logical and physical data models (dimensional, relational) across our Databricks lakehouse environment.
• Apply Kimball star schema and normalized modeling best practices.
• Translate business requirements into clear, well-documented data structures that serve analytics, reporting, and AI consumption.

Cross-System Data Lifecycle Awareness

• Model data with awareness of the full lifecycle—from systems of record through integration layers to lakehouse and downstream consumers.
• Ensure models account for how data originates, flows, and is consumed across multiple systems, not just within the big data platform.

Semantic Layer and AI-Ready Data

• Support the development of semantic layer artifacts (curated views, conformed dimensions, governed metrics, Genie-ready configurations) that enable AI agents and self-service analytics to interpret enterprise data correctly.
• Partner with the Principal Data Engineer to evolve metadata practices toward richer, machine-interpretable descriptions—business definitions, relationships, and constraints.

Data Governance and Standards

• Adhere to and help refine enterprise data modeling standards, naming conventions, and design patterns within the SDLC.
• Support model review processes, change management, and data quality efforts.
• Identify opportunities to improve modeling approaches or reduce duplication.

Collaboration and Delivery

• Partner with data engineers to implement models in performant ELT pipelines and denormalized views (such as Dealer Datahub patterns).
• Participate in design reviews, provide modeling guidance during development, and work cross-functionally with business stakeholders and analytics teams.

Competencies

• Customer Empathy: Understand the perspectives, pain points, and experiences of customers.
• Engineering Excellence: Bring great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business.
• One Team: Collaborate and communicate seamlessly across the organization, fostering a sense of collective purpose.
• Owner’s Mindset: Adopt a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain.

Requirements

• 8+ years of experience in data modeling, data architecture, or analytics engineering roles.
• Strong hands-on expertise in dimensional modeling (Kimball) and relational modeling (3NF).
• Experience modeling data across multiple source systems in cloud data platforms (Databricks or similar).
• Awareness of how data models serve downstream AI and analytics use cases, with willingness to deepen expertise in semantic layer design and AI-ready data structures.
• Proficient SQL skills and experience working closely with data engineers on ELT pipelines.
• Experience with data modeling and design tools (ER/Studio, ERwin, SqlDBM, dbt, or similar).
• Understanding of data governance concepts including lineage, metadata management, and data quality.
• Strong communication skills and ability to collaborate across technical and business teams.
• Self-directed and comfortable operating with autonomy while working within established architectural direction.
• Experience with or exposure to semantic layers, governed metrics, or analytics modeling frameworks.

Preferred

• Familiarity with metadata management platforms, data catalogs, or data documentation tools (e.g., Collibra, Unity Catalog).
• Awareness of knowledge graphs, ontologies, or formal data description frameworks (OWL, RDF)—curiosity and willingness to learn is essential.
• Hands-on Databricks experience including Unity Catalog, Delta Lake, and lakehouse architecture patterns.
• Experience in financial services, auto lending, or regulated industries.
• Exposure to AI-assisted tooling for metadata, documentation, or data profiling tasks.