Architect / Data Engineer - Hybrid

MSP•Chandler, AZ

7d•Hybrid

About The Position

Genesis10 is currently seeking an Architect / Data Engineer for a hybrid position (3 days onsite per week required) with a Global Financial Institution located in Chandler, AZ. This is a 12+ month contract opportunity. This role is responsible for leading the design, governance, and improvement of data and knowledge foundations that support AI and agentic capabilities across Network Services. The ideal candidate will work with both structured and unstructured data sources to ensure information is defined, organized, and governed for trusted AI consumption, partnering with subject matter experts to translate high-quality data into reusable AI-ready assets.

Requirements

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

Nice To Haves

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

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