Sr Data Modeler Job at O'Reilly Auto Parts

Job Description

The Sr Data Modeler is a key technical contributor responsible for designing, developing, and optimizing conceptual, logical, and physical data models across structured and semi-structured platforms including relational, NoSQL, and real-time systems. This role ensures data models are scalable, governed, and aligned with performance and business requirements. As a senior practitioner, the role partners closely with engineers, stakeholders, and product teams to translate domain-specific data needs into robust models for reporting, analytics, and AI use cases. The Senior Data Modeler also promotes modeling best practices, contributes to data governance efforts, and supports the implementation of hybrid table and streaming-aware data architectures.

Job Responsibility

Design domain-level conceptual, logical, and physical data models across OLTP and OLAP systems
Apply best practices in relational modeling using tools such as Erwin, dbt, and UML
Implement multi-model data environments that span relational, NoSQL, graph, and event-based systems
Develop dimensional models, normalized schemas, and de-normalized views
Collaborate with platform and engineering teams to ensure models support schema evolution and efficient query performance
Translate business requirements and analytics use cases into well-structured data models
Recommend modeling techniques and platform selection
Work closely with engineers and product owners to ensure model designs support KPI alignment
Lead and implement modeling requirements for feature stores and analytic datasets
Maintain detailed documentation including entity definitions, data dictionaries, model lineage
Contribute to the enforcement of modeling standards
Support governance efforts through consistent metadata management
Execute schema governance processes
Develop performant physical data models for Snowflake, BigQuery, PostgreSQL
Collaborate with data engineers to implement optimal indexing, clustering, partitioning
Contribute in troubleshooting performance issues
Support continuous improvement of data models
Work with engineering teams to embed models into ingestion pipelines
Validate that dbt models, ETL/ELT logic, and CI/CD deployment scripts accurately reflect designs
Support integration of models with real-time systems
Participate in quality assurance cycles
Contribute to the development of reusable semantic models
Help unify metric definitions and business logic across systems
Contribute to graph and document modeling efforts
Embed structural validation, referential integrity checks, and schema verification
Collaborate with engineers and platform teams to ensure data health monitoring is modeled
Support automated testing and CI/CD integration of models
Participate in resolving modeling-related issues
Serve as a mentor and resource to junior data modelers and engineers
Contribute to modeling playbooks, reusable templates, and internal knowledge repositories
Participate in technical reviews and modeling community of practice discussions
Stay up to date with modern modeling techniques

Requirements

Advanced experience designing logical and physical data models for OLTP, OLAP, and streaming systems
Strong experience in relational data modeling, including dimensional modeling (star/snowflake), data vault, and normalized structures using modeling tools such as Erwin or UML
Advanced competence in developing and managing data models across data platforms, such as Snowflake, BigQuery, PostgreSQL, and cloud SQL services
Experience with NoSQL and semi-structured data models (e.g., MongoDB, Cassandra)
Basic to intermediate experience with graph databases and modeling concepts (e.g., Neo4j)
Strong experience modeling for analytics and machine learning, including schema design for curated datasets, feature stores, and metric layers
Proficient in translating data contracts and business definitions into reusable semantic models
Experience incorporating streaming-aware modeling considerations
Advanced ability to work with product owners and business stakeholders
Strong understanding of enterprise business processes
Experience working in agile data product environments
Ability to anticipate business implications of schema changes
Experience leading data modeling efforts on cross-functional teams
Ability to mentor junior data modelers and analysts
Strong contributor to modeling playbooks
Experience aligning data models to enterprise taxonomies
Strong understanding of data modeling’s role in data governance
Advanced experience integrating semantic models and metrics stores
Ability to influence modeling direction
Education: Bachelor's Degree or Equivalent Level in Computer Science or related field
Experience: Experienced practitioner able to deal with the majority of situations and to advise others (3 to 6 years)
Managerial Experience: Basic experience coordinating the work of others (4 to 6 months)

Nice to have

Experience modeling for hybrid workloads, supporting both transactional and analytical use cases
Working knowledge of streaming and event-based modeling patterns, including Kafka schema registry integration
Familiarity with open table formats such as Apache Iceberg, Delta Lake, or Hudi
Exposure to lineage and metadata integration tools such as Alation, Collibra
Exposure in enabling LLM-ready data assets
Demonstrated ability to support platform migrations or modeling refactoring efforts

What we offer

Competitive Wages & Paid Time Off
Stock Purchase Plan & 401k with Employer Contributions Starting Day One
Medical, Dental, & Vision Insurance with Optional Flexible Spending Account (FSA)
Team Member Health/Wellbeing Programs
Tuition Educational Assistance Programs
Opportunities for Career Growth

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