Sr Data Modeler

Compensation

Pay Range: $ - $

The actual hourly rate will equal or exceed the required minimum wage applicable to the job location. Additional compensation includes annual, quarterly performance, or premiums may be paid in amounts ranging per hour in specific circumstances. Premiums may be based on schedule, facility, season, or specific work performed. Multiple premiums may apply if applicable criteria are met.

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, No

SQL, and real-time systems. This role ensures data models are scalable, governed, and aligned with performance and business requirements. As a senior practitioner, the team 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 promotes modeling best practices, contributes to data governance, and supports the implementation of hybrid table and streaming-aware data architectures.

• Design domain-level conceptual, logical, and physical data models across OLTP and OLAP systems, with emerging support for streaming and hybrid workloads.
• Apply best practices in relational modeling using tools such as Erwin, dbt, and UML, ensuring alignment with medallion or data mesh architecture principles.
• Implement multi-model data environments that span relational, No
  
  SQL, graph, and event-based systems.
• Develop dimensional models, normalized schemas, and de-normalized views for operational reporting, dashboarding, and analytical queries.
• Collaborate with platform and engineering teams to support schema evolution, model extensibility, and efficient query performance.
• Translate business requirements and analytics use cases into well-structured data models, ensuring semantic consistency across domains.
• Recommend modeling techniques and platform selection based on performance, data type, and user needs.
• Lead modeling requirements for feature stores and analytic datasets used in analytics, AI and machine learning pipelines.
• Maintain detailed documentation in cataloging tools such as Alation and Collibra.
• Contribute to the enforcement of modeling standards such as naming conventions, schema versioning, and semantic layering practices.
• Support governance efforts through consistent metadata management, model certification, and stewardship handoff documentation.
• Develop performant physical data models for Snowflake, Big Query, Postgre
  
  SQL, and other modern cloud-native data warehouses.
• Collaborate with data engineers to implement optimal indexing, clustering, partitioning, and table design strategies.
• Assist in troubleshooting performance issues related to model complexity, data skew, or inefficient joins.
• Embed models into ingestion pipelines, transformation layers, and semantic APIs.
• Validate that dbt models, ETL/ELT logic, and CI/CD deployment scripts accurately reflect logical and physical designs.
• Participate in quality assurance cycles reviewing test coverage and production readiness of model implementations.
• Contribute to reusable semantic models for metrics stores, self-service BI tools, and advanced analytics layers.
• Support graph and document modeling for product attribution, recommendation, or customer graph enrichment as needed.
• Embed structural validation and schema verification into the development lifecycle.
• Mentor junior data modelers and engineers, providing guidance on modeling fundamentals and SQL performance.

• Advanced experience designing logical and physical data models for OLTP, OLAP, and streaming systems focused on performance, extensibility, and platform standards.
• Strong experience in relational data modeling, including dimensional modeling, data vault, and normalized structures using tools such as Erwin or UML.
• Proficiency in Snowflake, Big Query, Postgre
  
  SQL, and cloud SQL services.
• Experience with No
  
  SQL and semi-structured data models (e.g., Mongo
  
  DB, Cassandra) and fit-for-purpose decisions.
• Basic to intermediate experience with graph databases such as Neo4j.
• Strong modeling for analytics and machine learning, including feature stores and metric layers.
• Proficient in translating data contracts and business definitions into reusable semantic models.
• Experience with streaming-aware modeling for schema evolution, partitioning, and idempotency.
• Advanced ability to work with product owners and stakeholders to translate business requirements into data entities and relationships.
• Experience leading data modeling efforts on cross-functional teams or key domain areas.
• Mentoring skills for junior data modelers and analysts.
• Experience with data governance, lineage, metadata, and compliance.
• Strong communication skills for discussing trade-offs with both technical and business audiences.
• Preferred: modeling for hybrid workloads, streaming, event-based…