Data Architect

Requirements

• 5+ years experience in data architecture, data modeling, analytics engineering, or a related role building production data models and semantic layers
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• SQL expert. Able to write efficient, maintainable SQL for complex transformations and to optimize performance in a cloud warehouse
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• Hands-on experience with dbt (or equivalent) and Git/CICD for data transformations and model deployment
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• Proven track record designing metrics/semantic layers and ensuring a “single source of truth” for business definitions
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• Experience with observability & lineage tooling, or designing data quality and audit processes for analytic/ML outputs
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• Strong cross-functional communication skills; comfortable translating ambiguous business questions into reliable data artifacts and enabling non-technical consumers
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• (Desirable) Experience working with AI/LLM workflows (RAG, embeddings, fine-tuning) or converting unstructured data into embeddings and retrieval assets
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• (Desirable) Familiarity with cloud warehouses (Snowflake, BigQuery, Databricks) and reverse-ETL patterns
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• (Desirable) Experience owning or contributing to data governance frameworks and enabling broad dbt access across functional teams

What the job involves

• Clio is hiring a Data Architect to design, build, and govern the data foundations that power company reporting, analytics, and our AI-first initiatives
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• 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
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• 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
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• 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)
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• 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
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• AI data strategy & unstructured data tooling: Design data lifecycles for AI use cases—ingestion, embedding, RAG, and model-centric data plumbing—and convert unstructured signals (transcripts, documents, emails) into AI-ready assets
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• 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
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• Governance & cross-team enablement. Partner with Data Engineering, RevOps, Product, and other stakeholders to set modeling standards, manage dbt access and ownership boundaries, and enable “self-service” analytics across the company
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• 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
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• Mentorship & standards. Help grow the team by setting modeling standards, reviewing peer work, and mentoring junior modelers/analysts