Senior Data Architect
Cooley AI is seeking a Senior Data Architect to join the Innovation team.
Position summary: As a leading technology law firm, Cooley is determined to become a leader in the digital practice of law. The Senior Data Architect is the authority on the canonical data model and Data Vault 2.0 methodology, who owns the architectural standards that every data pipeline, AI model, and analytics product built on the Northstar Lakehouse is dependent on. Working on a greenfield opportunity, the Senior Data Architect will work to establish Data Vault 2.0 as the governing methodology for Cooley's non-financial data domains, including client, matter, timekeeper, firm, and practice group, to build a canonical data model that will serve the firm's legal intelligence products. The primary focus of this role is to establish Data Vault 2.0 methodology standards and the canonical data model foundations. Specific duties and responsibilities include, but are not limited to, the following:
Position responsibilities:Canonical Data Model Ownership: - Own and maintain the firm's canonical data model for all non-financial data domains including client, matter, timekeeper, firm, practice group, and jurisdiction; defining the authoritative entity definitions, relationships, and attributes that all platform layers and consuming products build on
- Establish and enforce canonical data model standards across all five data verticals, providing architectural review and sign-off on any data product, pipeline, or AI feature that introduces new entities or modifies existing canonical definitions
- Partner closely with legal sector SMEs across the Intelligence & Client Services and Data Products functions to extract and formalize legal domain knowledge into canonical data model decisions understanding that business accuracy is as important as technical correctness
- Define data contracts between the canonical data model layer and consuming systems ensuring matter, client, and timekeeper entities are consumed consistently across AI models, analytics products, and data applications
- Maintain the canonical data model documentation as a living architectural artifact including entity definitions, relationship cardinality, attribute standards, and business rule encoding updated as the platform and legal domain understanding evolves
- Govern canonical entity changes through a structured change control process, assessing downstream impact across pipelines, products, and AI models before any canonical definition is modified
Data Vault 2.0 Methodology Leadership: - Establish Data Vault 2.0 as the governing methodology for the Silver Layer Raw Vault across all non-financial data domains - defining hub, link, and satellite design standards, hash key strategies, satellite grain and historization patterns, and load pattern conventions the Data Engineering team implements consistently
- Design the Raw Vault architecture for core legal sector entities including client hub, matter hub, timekeeper hub, and their associated links and satellites with enough domain context to reflect how these entities behave in a law firm environment
- Define Business Vault patterns for the legal sector including computed satellites, point-in-time tables, bridge tables, and exploration links that serve the analytical and AI consumption patterns the platform needs
- Train the Data Engineering team in Data Vault 2.0 methodology establishing the firm's DV standards through hands-on guidance, code review, and architectural decision documentation
- Collaborate with the Senior Data Architect, MDM & Data Modeling to ensure the Raw Vault hub design aligns with the canonical MDM master data model
- Design the integration between the Data Vault Silver Layer and the gold layer dimensional models and semantic layer - ensuring Business Vault structures feed cleanly into the gold layer consumption patterns
- Develop familiarity with DV-native quality patterns including contributing to how data quality rules, exception handling, and observability are applied within a Data Vault load pattern in collaboration with the Platform Governance & Quality function
Platform Architecture Standards & Cross-Cutting Authority: - Hold cross-cutting architectural authority across all five data verticals. Own the architectural decisions affecting the canonical data model, Silver Layer Raw Vault design, or platform-wide data standards
- Establish and maintain the firm's data architecture decision record documenting architectural decisions, context, considered alternatives, and implications for future platform evolution
- Define the platform's approach to data modeling across all layers including Raw Vault in the Silver Layer, dimensional models and semantic layer in the gold layer, and Lakebase transactional models for product databases. Ensure each layer's design philosophy is coherent and the transitions between layers are clean
- Govern the technical data analyst EPIC analysis workflow from an architectural standards perspective, ensuring Silver Layer Raw Vault coverage assessments performed by the analyst team accurately reflect the canonical DV architecture. Communicate gaps to Data Engineering with sufficient technical context
- Partner with the Platform Architect in the Data Engineering & Platform function on infrastructure architecture decisions that affect data platform design - Unity Catalog metastore configuration, Delta Lake table properties, and Databricks workspace architecture
- Contribute to the firm's AI readiness architectural standards, ensuring canonical entities, Raw Vault structures, and Business Vault patterns are designed for LLM consumption, RAG pipeline data preparation, and vector search integration from the outset
Legal Domain Knowledge Extraction & SME Collaboration: - Build structured working relationships with legal sector SMEs across the Intelligence & Client Services and Data Products functions - systematically extracting the domain knowledge needed to make canonical data model decisions that are legally and operationally accurate
- Develop deep working knowledge of how Cooley's core legal data domains work in practice, including how matters open, develop, and close; how client relationships are structured; how timekeepers relate to matters and billing; how practice group taxonomy maps to firm organization and league table classification
- Partner with the Intelligence & Client Services and Data Products functions on competitive intelligence and legal intelligence data structures - ensuring the canonical data model captures the entity relationships needed for CI analytics, league table construction, and legal market intelligence products
- Work with the Data Products function's Technical Data Analysts to understand what canonical entity coverage is needed for each EPIC - using EPIC analysis outputs to identify gaps in the canonical model before they become pipeline dependencies
- Engage with 3E and operational system SMEs to understand the legal billing and matter management data landscape informing canonical entity design with knowledge of how source system data behaves rather than how it is theoretically structured
Data Engineering Team Development & Standards Enforcement: - Serve as the primary Data Vault methodology resource for the Data Engineering team by providing hands-on guidance, architectural review, and DV pattern coaching as the engineering team is built with Data Vault as the governing methodology
- Review Data Engineering pipeline implementations for DV standards compliance - hash key generation, satellite grain correctness, link cardinality, load date handling, and record source population - catching methodology errors before they compound into architectural debt
- Develop Data Vault implementation guides, pattern libraries, and code standards documentation that the engineering team can build to consistently - reducing the principal bottleneck risk of having a single DV methodology authority in a growing team
- Partner with the Senior Data Engineering Manager on engineering team capability development, providing DV methodology input into engineering hiring assessments and onboarding standards
- Collaborate with the DataOps Engineer on CI/CD pipeline standards for Data Vault loads - ensuring DV load patterns are implemented in automated, testable, and deployable pipeline code rather than ad hoc scripts
- All other duties as assigned or required
Required: - After orientation at Cooley LLP, exhibit proficiency in the Microsoft Office suite, iManage, and other firm applications
- Ability to work extended and/or weekend hours, as required
- Ability to travel, as required
- 10+ years of data architecture experience with demonstrated ownership of canonical data model design and Data Vault 2.0 implementation in production enterprise environments
- Deep Data Vault 2.0 methodology expertise, and production experience designing and implementing Raw Vault (hubs, links, satellites), Business Vault patterns (computed satellites, PIT tables, bridge tables), Information Mart structures, and hash key strategies in a cloud data platform context
- Hands-on experience with Databricks - Delta Lake, Unity Catalog, Databricks SQL, and medallion architecture implementation with direct ownership of Silver Layer design
- Demonstrated canonical data model ownership at enterprise scale - defining authoritative entity definitions, relationship standards, and data contracts across multiple consuming systems and organizational functions
- Strong cross-functional architectural authority - experience holding and enforcing architectural standards across teams that do not directly report to you
- Proven SME collaboration capability - structured approach to extracting business domain knowledge from subject matter experts and translating it into canonical data model decisions
- Experience establishing data architecture standards in a greenfield or rebuilding environment - writing architectural decision records, pattern libraries, and implementation guides that a growing engineering team can work from
- Advanced SQL proficiency and working knowledge of dbt for transformation layer review and standards enforcement
- Experience training or developing engineers in a data modeling methodology, including Data Vault, dimensional modeling, or comparable structured methodology at a team capability development level
- Comfort using GitHub for version control, architectural documentation, and peer review in a collaborative data engineering context
- Demonstrated Agile fluency including sprint ceremonies, architectural spike work, and definition of done standards in a squad-based delivery model
Preferred: - Law firm, legal tech, or legal services domain knowledge; understanding of matter lifecycle, timekeeper and billing data, client relationship structures, practice group taxonomy, or comparable legal sector data complexity
- Demonstrated ability to rapidly develop legal sector domain expertise through structured SME engagement
- Experience with 3E Elite Enterprise or comparable legal billing and matter management system and an understanding of how operational legal system data structures relate to canonical entity design
- Familiarity with MDM canonical model design and how master data entity definitions inform Raw Vault hub design with experience working alongside an MDM architect to ensure DV hub design and MDM golden record design are aligned
- Experience designing Data Vault structures for AI consumption including Raw Vault and Business Vault patterns optimized for LLM data preparation, RAG pipeline inputs, and feature store integration
- Familiarity with Databricks Genie, Databricks AI/BI, or comparable AI-native query interface and how canonical data model and semantic layer definitions power natural language query capabilities
- Experience with Delta Share or intercompany data sharing patterns
- DAMA-DMBOK, CDMP, or Data Vault 2.0 certification (Dan Linstedt methodology)
- AI-assisted development fluency including Claude Code, GitHub Copilot, or comparable tools used to accelerate architectural documentation, DV pattern library development, and standards enforcement workflows
Competencies: - Domain knowledge extractor who builds structured working relationships with SMEs; asks the right questions, listens carefully, and translates business domain complexity into canonical data model precision
- Greenfield builder that's comfortable with establishing methodology from scratch
- Ability to work cross-functionally with authority and without hierarchy, holding architectural standards across verticals through expertise and relationship rather than management authority
- Innovative mindset with the ability to operate effectively in a lean, fast-moving environment
- Excellent attention to detail
- Exceptional interpersonal and communication skills (both technical and non-technical) with ability to facilitate and solve problems
