Remote Data Modeler Jobs in San Ramon, CA (NOW HIRING)

Job Description:

We are looking for a highly experienced Senior Data Architect / Data Modeler to fill a critical hands-on role as part of a data architecture team supporting a large-scale enterprise platform transformation. This role is heavily focused on enterprise data modeling, including conceptual, logical, and physical modeling across both transactional application databases and downstream analytical data platforms.

The ideal candidate is an expert data modeler with deep experience designing normalized transactional databases, preferably using Postgres, and a strong understanding of how well-designed operational data models support scalable applications, clean integration patterns, analytics, governance, and future AI-enabled capabilities.

The ideal candidate should also be comfortable working in modern AI-assisted engineering environments, including the use of agentic coding tools to accelerate design, implementation, refactoring, documentation, and review activities. These tools may include platforms such as Claude Code, Cursor, OpenAI Codex, or similar AI coding assistants. The candidate must understand how to use these tools productively while maintaining strong architectural control, code quality, data modeling discipline, security awareness, and human review of generated outputs.

This is a hands-on architecture role. The successful candidate must be able to move fluidly from business concepts and canonical/logical models to detailed physical schemas, keys, constraints, indexing strategies, entity relationships, and implementation-ready database designs.
What You Will Do:

• Data Modeling and Enterprise Data Design - Lead the creation of conceptual, logical, and physical data models that accurately represent core business entities, relationships, transactions, and data flows. Develop models that are durable, scalable, and reusable across application, integration, analytics, and governance use cases.

• Transactional Database Architecture - Design normalized transactional databases supporting enterprise-scale applications, with a strong emphasis on 3NF modeling, referential integrity, data quality, extensibility, performance, and maintainability. Translate business and application requirements into robust Postgres physical database designs.

• Postgres Physical Modeling and Design - Create implementation-ready physical data models for Postgres, including tables, relationships, keys, constraints, indexes, data types, naming standards, and performance-oriented design patterns. Partner with engineering teams to ensure the physical database design supports scale, high throughput, application reliability, and long-term maintainability.

• AI-Assisted and Agentic Development Practices - Use AI-assisted and agentic coding tools where appropriate to accelerate database design and engineering activities, including DDL generation, schema refactoring, migration scripts, SQL review, documentation, test data generation, data quality checks, and model-to-code translation. Apply expert human review to all AI-generated outputs to ensure correctness, performance, security, maintainability, and alignment with approved data architecture standards.

• Full Lifecycle Architecture Delivery - Participate across the full solution lifecycle, including requirements analysis, domain modeling, logical design, physical design, design reviews, implementation support, migration planning, refactoring, testing, and production stabilization. Support multiple projects simultaneously while maintaining consistency with enterprise data architecture standards. Leverage AI-assisted and agentic coding tools to improve delivery speed and consistency where appropriate, while ensuring that architectural decisions, model quality, database design, and production readiness remain under expert human control.

• Analytical Data Architecture - Design and support downstream analytical structures in Databricks or Snowflake, including medallion architecture, curated data layers, dimensional models, facts, dimensions, and conformed structures following Kimball methodology.

• Data Integration and Platform Alignment - Define how transactional data structures integrate with downstream data platforms, data warehouses, reporting environments, and AI/ML use cases. Ensure that operational models are designed with clean integration, lineage, governance, and analytical consumption in mind.

• Data Architecture Standards and Governance - Contribute to enterprise data architecture standards, modeling conventions, naming standards, metadata practices, data quality expectations, and governance processes. Ensure models are aligned with business definitions, enterprise standards, and long-term architectural direction.

• Stakeholder Collaboration - Work closely with product managers, software engineers, business stakeholders, data engineers, BI teams, and governance teams to translate complex business processes into clear, precise, and scalable data models.

• Technical Leadership and Mentoring - Provide technical leadership to engineering and data teams on data modeling, relational design, database normalization, dimensional modeling, AI-assisted development practices, and data architecture best practices. Review and challenge designs where needed to ensure architectural quality.

Qualifications:

• 10+ years of experience as a Data Architect, Data Modeler, or similar role, with significant hands-on responsibility for enterprise data modeling.
• Expert-level experience creating conceptual, logical, and physical data models.
• Deep expertise in relational data modeling, including 3NF, normalization, entity relationship modeling, keys, constraints, referential integrity, and physical schema design.
• Significant experience designing transactional application databases supporting enterprise applications, preferably for high-scale or high-throughput environments.
• Strong experience with Postgres physical database design, including tables, constraints, indexes, data types, performance considerations, and scalable schema design.
• Demonstrated experience delivering data architecture work across the full lifecycle of multiple projects, from requirements and logical modeling through physical implementation and production support.
• Strong SQL skills, including the ability to read, write, review, and optimize SQL in support of data architecture and database design.
• Experience with enterprise modeling tools such as Erwin, ER/Studio, or similar tools.
• Strong understanding of Kimball dimensional modeling, including facts, dimensions, grain definition, conformed dimensions, surrogate keys, slowly changing dimensions, and dimensional warehouse design.
• Experience with modern analytical platforms such as Databricks or Snowflake.
• Understanding of medallion architecture, including bronze, silver, and gold data layers.
• Proficiency with modern AI-assisted and agentic coding tools such as Claude Code, Cursor, GitHub Copilot, GitHub Copilot coding agents, OpenAI Codex, Devin, or similar tools.
• Experience using AI coding tools to support database development, SQL generation, schema design, migration scripting, code review, documentation, testing, or refactoring activities.
• Strong judgment in reviewing, validating, correcting, and governing AI-generated code, database objects, documentation, and technical artifacts.
• Ability to use agentic coding tools without compromising data architecture standards, modeling discipline, security practices, performance, or production quality.
• Experience defining data integration patterns between operational systems, data platforms, warehouses, BI tools, and downstream consumers.
• Strong knowledge of data management best practices, including metadata, lineage, data quality, master/reference data, and governance.
• Ability to translate ambiguous business requirements into precise, structured data models and implementation-ready designs.
• Excellent communication skills, with the ability to explain modeling decisions to both technical and non-technical stakeholders.
• Ability to challenge poor design decisions constructively and advocate for sound data architecture practices.
• Experience mentoring engineers, data modelers, or data architects on relational modeling and database design best practices.
• Certifications in data architecture or related areas, such as DAMA/CDMP, TOGAF, cloud data platform certifications, or database certifications, are desirable but not required.