Senior Data Architect

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The posting includes: About Cooley AI (maybe "About the role"? Actually "About Cooley AI" is about company, not role. The "Position summary" is about role. We can treat "About the role" as position summary. Responsibilities: many bullet points. Requirements: list of required qualifications. Qualifications: maybe same as requirements? The posting includes "Required" list and "Preferred" list. So we can have Requirements (required) and maybe Qualifications (preferred) or Skills. Benefits, Pay, Schedule: not mentioned. So we should not include those sections. We need to use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate, navigation, etc. Also remove duplicated lines. We need to output only HTML fragment. Let's extract content: About Cooley AI: "Cooley AI is the wholly-owned subsidiary of Cooley LLP. The frontier development arm of the leading technology law firm in the world. Our mission is to bring the best-in-class practices from the technology field into the legal domain to help lead our firm and the legal industry into the AI era. All employees of Cooley AI are employees of Cooley and are seconded to Cooley AI." 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." Then "Specific duties and responsibilities include, but are not limited to, the following:" then a big list of responsibilities broken into sections: Canonical Data Model Ownership, Data Vault 2.0 Methodology Leadership, Platform Architecture Standards & Cross-Cutting Authority, Legal Domain Knowledge Extraction & SME Collaboration, Data Engineering Team Development & Standards Enforcement, All other duties as assigned or required. We need to capture responsibilities as list items. Could group under a single

  with each duty as
  • . But we might want to preserve subheadings? The instruction: use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So only those headings allowed. So we cannot have subheadings like "Canonical Data Model Ownership". We must embed them within responsibilities list maybe as part of

    text. Thus we will have:

    About the role

    ... position summary ...

    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

    Now Requirements: The "Required" list after responsibilities. Let's extract required qualifications: - 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 We'll put these in a

    under

    Requirements

    . Preferred qualifications: list after "Preferred". Let's extract: - 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/B (cut off). The text ends abruptly: "Familiarity with Databricks Genie, Databricks AI/B". Probably