Sr Data Modeler

Compensation

Pay Range: $108,086.00 - $180,144.00 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, 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. Responsibilities and Duties: 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 (e.g., Snowflake, BigQuery, PostgreSQL), NoSQL (MongoDB, Cassandra), graph (e.g., Neo4j), and event-based (e.g., Kafka, Pub/Sub) systems. Develop dimensional models, normalized schemas, and de-normalized views tailored for operational reporting, dashboarding, and analytical queries. Collaborate with platform and engineering teams to ensure models 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 (relational vs NoSQL vs streaming) based on performance, data type, and user needs. Work closely with engineers and product owners to ensure model designs support KPI alignment, reusability, and future-state scalability. Lead and implement modeling requirements for feature stores and analytic datasets used in analytics, AI and machine learning pipelines. Maintain detailed documentation including entity definitions, data dictionaries, model lineage, and change logs in cataloging tools (e.g., Alation, 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. Execute schema governance processes to ensure backward compatibility and data trust across ingestion and consumption layers. Develop performant physical data models for Snowflake, BigQuery, PostgreSQL, and other modern cloud-native data warehouses and platforms. Collaborate with data engineers to implement optimal indexing, clustering, partitioning, and table design strategies. Contribute in troubleshooting performance issues related to model complexity, data skew, or inefficient joins in reporting and data science pipelines. Support continuous improvement of data models by analyzing access patterns, profiling large datasets, and proposing schema refinements. Work with engineering teams to 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. Support integration of models with real-time systems (e.g., Kafka, Pub/Sub) and ensure models function across batch and streaming environments. Participate in quality assurance cycles by reviewing test coverage, edge case handling, and production readiness of model implementations. Contribute to the development of reusable semantic models for metrics stores, self-service BI tools, and advanced analytics layers. Help unify metric definitions and business logic across systems through dimensional modeling and modular dbt workflows. Work with analytics engineers to align modeling logic with metric stores and dashboards, enabling consistent performance and insight delivery. Contribute to graph and document modeling efforts as needed for use cases such as product attribution, recommendation, or customer graph enrichment. Embed structural validation, referential integrity checks, and schema verification into the development lifecycle for all new data models. Collaborate with engineers and platform teams to ensure data health monitoring (e.g., freshness, null tracking, type mismatches) is modeled at the sch Apply tot his job Apply To this Job