Senior Data Architect, Legal Operations

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there"s no telling what you could accomplish.\\n\\nAre you passionate about building data foundations that power intelligent systems and strategic decision-making?\\nDo you thrive at the intersection of data architecture, governance, and enterprise transformation?\\nCan you translate complex data challenges into elegant, scalable solutions?\\n\\nThe Applied Data Science team within Legal Operations is building the data foundation that powers AI and analytics for a global legal organization. We"re transforming how legal work gets done — and it starts with trusted, connected data. The Principal Data Architect leads the Data Management Office (DMO) and owns the data architecture vision that makes AI accurate and analytics reliable.

The Senior Data Architect owns the data architecture vision for Legal Operations and leads the Data Management Office. You will design and implement the semantic layer, unified data model, and data governance frameworks that make data trusted, connected, and ready for AI and analytics consumption. This role combines strategic architecture leadership with hands-on technical work and team leadership.

Define and own the enterprise data architecture for Legal Operations, including the unified data model, semantic layer, and integration patterns\\nDesign and implement entity resolution to create master records for key entities (law firms, vendors, timekeepers, counterparties) across all legal systems\\nEstablish common language, taxonomy, and legal ontology that standardizes terminology across practice groups and systems\\nArchitect the data lake and integration layer that connects matter management, contracts, eBilling, document management, and other legal systems\\nBuild the data foundation that enables AI systems to achieve production-level accuracy and analytics to deliver trusted insights — providing the context, entity relationships, and data quality required\\nEstablish data quality standards, validation rules, and monitoring to ensure data accuracy and completeness\\nDesign and implement data governance frameworks including data ownership, access controls, lineage, and compliance\\nPartner with Data Stewards embedded in practice groups to drive data quality and adoption of standards\\nLead and mentor the DMO team, including Senior Data Engineer and Data Analysts\\nSet technical direction and standards for the team; review designs and implementations

Bachelor"s degree in Computer Science, Data Science, Information Systems, or related field (or equivalent experience); Master"s degree preferred\\n10+ years of experience in data architecture, data engineering, or related fields, with at least 4 years in a senior architecture or leadership role\\nDeep expertise in enterprise data architecture including data modeling, dimensional modeling, data lake design, and ETL/ELT patterns\\nStrong experience with cloud data platforms, particularly Snowflake\\nProficiency in SQL and Python for data engineering, transformation, and automation\\nExperience with data integration and transformation tools (dbt, Fivetran, or similar)\\nExperience designing and implementing entity resolution, master data management, or data quality solutions\\nProven experience leading and mentoring data teams\\nExcellent communication skills — able to translate data architecture concepts for technical and non-technical audiences

Experience building data architectures for legal tech or legal operations environments\\nFamiliarity with legal systems including matter management, CLM, eBilling, or document management systems\\nExperience with semantic layer technologies (dbt Semantic Layer, Cube, AtScale), knowledge graphs (Neo4j), or ontology design\\nUnderstanding of how data architecture enables AI/ML systems, including RAG, embeddings, vector databases, and context management\\nExperience with data observability and quality tools (Monte Carlo, Great Expectations, or similar)\\nKnowledge of data privacy, compliance, and governance frameworks relevant to legal data