Data Architect

15 hours ago

Chandler, AZ, United States Genesis10 Full-time

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Genesis10 is currently seeking a Data Architect / Data Engineer - Hybrid for a 12+ month contract position with a Global Financial Institution located in Chandler, AZ.

This role is responsible for developing, governing, and improving the data and knowledge foundations that support AI and agentic capabilities across Network Services. The individual in this role works across structured and unstructured data sources to ensure information is defined, organized, governed, and made available in a manner suitable for AI consumption. This role is accountable for helping establish standards, guidelines, and guardrails for how operational knowledge and other contextual assets are curated and prepared for trusted model use.

Responsibilities:

Develop and maintain data and knowledge assets that support AI and agentic use-cases across Network Services, including both structured and unstructured sources
Define and apply standards for how documentation, configurations, telemetry, metadata, policies, standards, and operational knowledge should be organized 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 implement scalable methods for storing, governing, indexing, and retrieving context assets needed for AI-enabled workflows and solutions
Establish and improve preventative and detective controls that identify and reduce data quality, metadata quality, knowledge quality, and freshness issues before they affect downstream use
Contribute to data quality remediation efforts by identifying upstream sources of issues, defining corrective actions, and improving the reliability of data at source
Define and maintain expectations for data ownership, lineage, freshness, stewardship, and governance across relevant data and knowledge domains
Build and enhance pipelines, transformations, validation routines, and supporting services that improve the quality and accessibility of data and knowledge assets
Support the creation of reusable templates, patterns, and guidance for knowledge artifacts, documentation standards, and context management practices
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 issues, gaps, and remediation priorities
Contribute to continuous improvement of data and knowledge management practices that strengthen trust, reuse, traceability, and operational supportability of AI context
Document data definitions, controls, transformations, and usage considerations so that downstream teams can reliably consume and support the resulting assets

Requirements:

Experience engineering and managing both structured and unstructured data used for analytics, automation, search, or AI-enabled solutions
Strong understanding of data modeling, transformation, storage, and retrieval patterns needed to support scalable and governed data pipelines
Ability to define and apply standards for how data, documents, knowledge artifacts, metadata, and operational context should be prepared for AI consumption
Experience establishing data quality controls, validation checks, and monitoring approaches that improve trust, completeness, consistency, and usability of data assets
Knowledge of preventative and detective controls used to identify, prevent, and remediate data quality, metadata, or knowledge management issues
Experience working with knowledge sources such as documentation, standards, policies, configurations, runbooks, telemetry, and operational records
Ability to work with product and service subject matter experts to understand domain context and translate network operational knowledge into usable data and knowledge assets
Working understanding of network technologies, infrastructure concepts, and operational service models sufficient to interpret domain-specific context and data requirements
Experience defining data ownership, lineage, freshness, retention, governance, and stewardship expectations in a regulated enterprise environment
Familiarity with AI and retrieval-oriented data preparation concepts, including grounding, context structuring, metadata quality, and content readiness for model use
Ability to design and implement scalable approaches for storing, governing, indexing, and retrieving data and knowledge assets for downstream use

Data Architect

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