Job Description & How to Apply Below
Requirements
- 5+ years experience in data architecture, data modeling, analytics engineering, or a related role building production data models and semantic layers ,
- SQL expert. Able to write efficient, maintainable SQL for complex transformations and to optimize performance in a cloud warehouse ,
- Hands-on experience with dbt (or equivalent) and Git/CICD for data transformations and model deployment ,
- Proven track record designing metrics/semantic layers and ensuring a “single source of truth” for business definitions ,
- Experience with observability & lineage tooling, or designing data quality and audit processes for analytic/ML outputs ,
- Strong cross-functional communication skills; comfortable translating ambiguous business questions into reliable data artifacts and enabling non-technical consumers ,
- (Desirable) Experience working with AI/LLM workflows (RAG, embeddings, fine-tuning) or converting unstructured data into embeddings and retrieval assets ,
- (Desirable) Familiarity with cloud warehouses (Snowflake, Big Query, Databricks) and reverse-ETL patterns ,
- (Desirable) Experience owning or contributing to data governance frameworks and enabling broad dbt access across functional teams
- Clio is hiring a Data Architect to design, build, and govern the data foundations that power company reporting, analytics, and our AI-first initiatives ,
- You’ll turn raw product and business data (structured and unstructured) into high-quality, documented, and governed datasets and a semantic layer that enables analysts, data scientists, and AI agents to trust a single source of truth ,
- This is a hands-on senior engineering role that blends deep SQL and modeling expertise with product sense, cross-functional collaboration, and ownership for standards and observability ,
- Design & deliver modular, production-grade data models:
Translate raw data into well-tested, performant datasets and reusable building blocks for downstream consumers (analytics, dashboards, ML) , - Build and govern the semantic layer:
Define and maintain standardized business metrics and semantic objects so teams and AI systems share a single source of truth , - AI data strategy & unstructured data tooling:
Design data life cycles for AI use cases—ingestion, embedding, RAG, and model-centric data plumbing—and convert unstructured signals (transcripts, documents, emails) into AI-ready assets , - Model observability & lineage. Implement monitoring, lineage, and auditability so we can trace what data influenced an analytic or AI output and detect data quality regressions ,
- Governance & cross-team enablement. Partner with Data Engineering, Rev Ops, Product, and other stakeholders to set modeling standards, manage dbt access and ownership boundaries, and enable “self-service” analytics across the company ,
- Engineering best practices. Apply software engineering principles to the data stack: dbt or similar transformation frameworks, Git, CI/CD, environments (dev/stage/prod), testing, and code review ,
- Mentorship & standards. Help grow the team by setting modeling standards, reviewing peer work, and mentoring junior modelers/analysts
Position Requirements
5+ Years
work experience
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