Manager, Data Governance & Knowledge Management

Description

This role ensures Finance can safely and confidently scale analytics and AI by building the data and knowledge foundations that make intelligence explainable, auditable, and trusted. It is a critical enabler of Finance Transformation and a key pillar of responsible AI adoption.

In this role, success means:

- Finance data is trusted, measurable, and consistently governed.

- Finance knowledge (rules, policies, KPIs, controls) is structured and reusable, not locked in documents or tribal knowledge.

- AI, analytics, and automation initiatives launch faster and with higher confidence because data and knowledge foundations are in place.

- Finance users trust AI outputs because they are explainable and correctable.

- Tangible progress is made toward AI readiness through delivered capabilities-not just frameworks.","responsibilities":"mplement and operate Finance-aligned data governance and data quality frameworks, including ownership, definitions, quality rules, lineage, and issue resolution.

Partner with IS&T to align Finance governance needs with enterprise platforms, certified data layers, access controls, and compliance requirements.

Establish automated data quality monitoring for Finance-critical datasets used in reporting, automation, and AI use cases.

Develop data quality scorecards and metrics that demonstrate business impact and support explainability and trust.

Ensure Finance data is traceable, documented, and auditable to support SOX, controls, and regulatory needs.

Design and maintain Finance knowledge representations (semantic models, metadata, ontologies, knowledge graphs) that capture Finance logic, rules, KPIs, controls, and processes.

Structure and prepare unstructured Finance data (policies, procedures, narratives, documentation) for AI consumption, including retrieval-augmented generation and agent-based workflows.

Ensure Finance knowledge assets are discoverable, contextualized, and versioned, enabling explainable and trustworthy AI outputs.

Partner with AI/ML and Automation teams to ensure data and knowledge structures support including Explainability, Human-in-the-loop validation, and Safe iteration and feedback-driven improvement

Enable mechanisms for Finance users and SMEs to understand, validate, and correct AI- and analytics-driven insights.

Ensure outputs can be traced back to source data, business rules, and knowledge artifacts.

Embed governance and explainability requirements into Finance analytics, automation, and AI delivery workflows from the start.

Lead and mentor a small team of data engineers focused on Data quality automation, Metadata and lineage management, and Knowledge modeling and graph development

Translate Finance and Transformation priorities into clear, actionable technical work.

Balance hands-on contribution with people leadership, code/design review, and delivery accountability.

Prioritize work based on Finance Transformation initiatives and AI-readiness milestones.

Partner closely with, Finance Product and Engineering teams, Program and Process leaders, IS&T data and platform teams

Work with Finance SMEs (FP&A, Accounting, Controllers, Audit, Risk) to accurately capture and formalize domain knowledge.

Participate in enterprise governance forums as the Finance representative, ensuring alignment without owning enterprise-wide governance scope.

Preferred Qualifications

Experience implementing data quality frameworks, metadata management, lineage, and data catalogs.

Hands-on experience or strong exposure to semantic modeling, ontologies, or knowledge graphs.

Familiarity with AI/ML data requirements, including explainability, RAG, and human-in-the-loop feedback.

Experience working with modern data platforms (e.g., Snowflake, Dataiku, cloud-native stacks).

Strong ability to translate technical concepts into Finance and business context.

Experience with knowledge graph or semantic technologies (tool-agnostic).

Exposure to AI/ML governance or model input management.

Experience supporting audits, compliance, or SOX-related data processes.

Execution-oriented leadership with strong attention to detail

Ability to balance technical depth with business understanding

Strong collaboration and stakeholder management skills

Problem-solving mindset with a bias toward automation and scalability

Clear written and verbal communication across technical and non-technical audiences

Experience working in Finance or regulated environments

Minimum Qualifications

8+ years of experience in data governance, data management, analytics engineering, or related roles

2+ years proven experience managing, developing and coaching as leader of a team

Bachelors of Science (BS) or equivalent degree in Finance, Business Management, Computer Science, Engineering or a field required.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

Client-provided location(s): Austin, TX

Job ID: apple-200640836-0157_rxr-660

Employment Type: OTHER

Posted: 2026-01-17T19:19:54