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Apple

Senior Data Architect, Legal Operations

Cupertino, CA$184,700 – $277,600Posted 20 days ago

Apply on company site View full job

Why you should apply for a job to Apple:

Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity

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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.

Are you passionate about building data foundations that power intelligent systems and strategic decision-making?

Do you thrive at the intersection of data architecture, governance, and enterprise transformation?

Can you translate complex data challenges into elegant, scalable solutions?

The 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.

Description

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.","responsibilities":"Define and own the enterprise data architecture for Legal Operations, including the unified data model, semantic layer, and integration patterns

Design and implement entity resolution to create master records for key entities (law firms, vendors, timekeepers, counterparties) across all legal systems

Establish common language, taxonomy, and legal ontology that standardizes terminology across practice groups and systems

Architect the data lake and integration layer that connects matter management, contracts, eBilling, document management, and other legal systems

Build 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

Establish data quality standards, validation rules, and monitoring to ensure data accuracy and completeness

Design and implement data governance frameworks including data ownership, access controls, lineage, and compliance

Partner with Data Stewards embedded in practice groups to drive data quality and adoption of standards

Lead and mentor the DMO team, including Senior Data Engineer and Data Analysts

Set technical direction and standards for the team; review designs and implementations

Preferred Qualifications

Experience building data architectures for legal tech or legal operations environments

Familiarity with legal systems including matter management, CLM, eBilling, or document management systems

Experience with semantic layer technologies (dbt Semantic Layer, Cube, AtScale), knowledge graphs (Neo4j), or ontology design

Understanding of how data architecture enables AI/ML systems, including RAG, embeddings, vector databases, and context management

Experience with data observability and quality tools (Monte Carlo, Great Expectations, or similar)

Knowledge of data privacy, compliance, and governance frameworks relevant to legal data

Minimum Qualifications

Bachelor's degree in Computer Science, Data Science, Information Systems, or related field (or equivalent experience); Master's degree preferred

10+ years of experience in data architecture, data engineering, or related fields, with at least 4 years in a senior architecture or leadership role

Deep expertise in enterprise data architecture including data modeling, dimensional modeling, data lake design, and ETL/ELT patterns

Strong experience with cloud data platforms, particularly Snowflake

Proficiency in SQL and Python for data engineering, transformation, and automation

Experience with data integration and transformation tools (dbt, Fivetran, or similar)

Experience designing and implementing entity resolution, master data management, or data quality solutions

Proven experience leading and mentoring data teams

Excellent communication skills - able to translate data architecture concepts for technical and non-technical audiences

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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