Data Governance Engineer (Chandler)

Description

This role is responsible for leading the design, governance, and continuous improvement of the data and knowledge foundations that support AI and agentic capabilities across Network Services. The individual in this role works across both structured and unstructured data sources to ensure information is defined, organized, governed, and made available in a manner suitable for trusted AI consumption. Within the Data & Knowledge pillar, this role provides senior-level leadership in defining standards, guidelines, and guardrails for how operational knowledge, documentation, configurations, telemetry, metadata, and other contextual assets are curated, controlled, and prepared for model use. This individual partners closely with product and service subject matter experts to understand network technologies, operational workflows, and domain context so that high-quality data and knowledge can be translated into reusable AI-ready assets. Compared with Data Engineer III, this role carries broader accountability for setting direction, influencing standards, designing scalable control frameworks, and guiding more complex cross-domain data and knowledge initiatives that improve the quality, trustworthiness, and operational supportability of context provided to models.

Key Responsibilities:• Lead the design and improvement of data and knowledge assets that support AI and agentic use-cases across Network Services, including both structured and unstructured sources.• Define and maintain standards for how documentation, configurations, telemetry, metadata, policies, standards, and operational knowledge should be organized, governed, and prepared for AI consumption.• Partner with product and service subject matter experts to understand network technologies, operational context, and domain-specific knowledge required to improve model grounding and decision quality.• Design and guide scalable methods for storing, governing, indexing, validating, and retrieving context assets needed for AI-enabled workflows and solutions.• Establish and evolve preventative and detective controls that identify and reduce data quality, metadata quality, knowledge quality, lineage, and freshness issues before they affect downstream AI use.• Lead remediation efforts for material data and knowledge quality issues by identifying upstream root causes, defining corrective actions, and improving reliability of source processes and assets.• Define expectations for ownership, stewardship, lineage, freshness, governance, and control accountability across relevant data and knowledge domains.• Build or guide the development of pipelines, transformations, validation routines, metadata structures, and supporting services that improve the quality and accessibility of AI-relevant context assets.• Create and improve reusable templates, patterns, and guidance for documentation, knowledge artifacts, metadata practices, and context management standards.• Work across engineering, architecture, operations, and governance teams to ensure data and knowledge practices align with enterprise controls, delivery needs, and approved standards.• Monitor and communicate the health, readiness, and quality of AI-relevant data and knowledge assets, including control gaps, remediation priorities, and material risks to trusted model consumption. See JD attachment for additional info.

Primary Skill Others - Please specify Secondary Skill Tertiary Skill Required Qualifications • Strong experience engineering and governing both structured and unstructured data used for analytics, automation, search, or AI-enabled solutions.• Advanced understanding of data modeling, transformation, storage, indexing, retrieval, and metadata management patterns needed to support scalable and governed data and knowledge pipelines.• Demonstrated ability to define enterprise-ready standards for how data, documents, knowledge artifacts, metadata, and operational context should be structured and prepared for AI consumption.• Experience establishing and improving data quality, metadata quality, and knowledge quality controls that increase trust, consistency, completeness, freshness, and usability of context assets.• Strong knowledge of preventative and detective controls used to identify, prevent, and remediate issues related to data quality, metadata quality, documentation quality, and knowledge management practices.• Experience working with knowledge sources such as policies, standards, configurations, telemetry, runbooks, architecture artifacts, and operational documentation.• Ability to work closely with product and service subject matter experts to interpret network operational knowledge and translate it into reusable, governed data and knowledge assets.• Strong working understanding of network technologies, infrastructure concepts, and service models sufficient to shape AI-ready context in partnership with technical domain experts.• Experience defining data ownership, stewardship, lineage, freshness, governance, and usage expectations in a regulated enterprise environment.• Familiarity with AI-oriented data and knowledge preparation concepts, including grounding, retrieval-readiness, context structuring, metadata enrichment, and content fitness for model use.• Ability to design scalable approaches for storing, governing, indexing, validating, and retrieving contextual assets used by AI-enabled workflows and solutions.• Strong analytical and problem-solving skills, including the ability to identify upstream causes of context quality issues and drive sustainable remediation across source processes and systems.• Experience leading or influencing cross-functional work across engineering, architecture, operations, governance, and business stakeholders to align data and knowledge practices to enterprise standards.• Experience mentoring less senior engineers or contributors and helping promote stronger engineering, control, and governance practices across a team or domain.• Strong written and verbal communication skills with the ability to document standards, controls, definitions, patterns, and usage guidance clearly for technical and non-technical audiences.• Experience working in a fast-paced and complex environment with evolving priorities, incomplete source data, and cross-functional dependencies.• Strong organizational discipline, technical judgment, and attention to detail in support of trusted, scalable, and production-grade data and knowledge practices.

Desired Qualifications

• Mentor less senior engineers and contributors on data engineering, knowledge engineering, control design, and context management practices to improve consistency and capability across the pillar.• Contribute to continuous improvement of data and knowledge management practices that strengthen trust, reuse, traceability, and operational supportability of AI context across the organization.• Document standards, definitions, controls, transformations, and usage considerations so that downstream teams can reliably consume, govern, and support resulting data and knowledge assets.