Senior Business Intelligence and Governance Architect

Boeing•Berkeley, MO

6d•Onsite

About The Position

Senior Business Intelligence and Governance Architect Company: The Boeing Company The Boeing Company’s Talent Analytics and Innovations team is currently seeking a Senior Business Intelligence and Governance Architect to join our team in Seattle, WA; Arlington, VA; North Charleston, SC or Berkeley, MO. The HR Data Steward ensures the quality, integrity, privacy, and usable governance of HR data and metric definitions across the employee lifecycle and supports responsible use of Artificial Intelligence (AI) / Machine Learning (ML) in Human Resources (HR). This role partners with HR Business Partners, HR Information Technology (IT), Finance, Data Science/ML Engineering, Analytics, Legal/Privacy, and business leaders to define standards, remediate data issues, and review models so HR data and AI outputs are accurate, fair, explainable, and compliant.

Requirements

10+ years of experience in human resources operations, human resources information systems, human resources analytics, or data governance
10+ years of experience in Business Intelligence/data analytics tools (Microsoft Power BI, Dashboards, SQL, Tableau, etc.)
5+ years of experience with SQL and Graph databases
5+ years of experience with privacy-preserving techniques (de-identification, synthetic data, access controls)

Nice To Haves

Bachelor’s degree in Human Resources, Information Management, Data Science, Business, or related field (or equivalent experience)
Experience interpreting model and fairness metrics and translate technical findings into business risk and mitigation actions
Experience with AI/ML model governance or model review responsibilities
Experience with ML lifecycle concepts, model registries, and explainability/fairness tools (examples: MLflow, model registries, SHAP, LIME, AIF360)
Strong analytical, problem-solving, and documentation skills

Responsibilities

Define, document, and enforce HR data governance policies, data definitions, metadata, and lineage
Maintain a data dictionary and feature catalog for HR domains, including metric definitions and 3rd party datasets
Act as subject-matter expert for HR data elements (hire-to-retire), data flows, and master records
Monitor, measure, and improve data quality (completeness, accuracy, consistency, timeliness, uniqueness); investigate root causes and coordinate remediation
Implement data-validation rules, data-quality dashboards, Key Performance Indicators (KPI), and Service Level Agreement tracking
Manage exception and reconciliation processes with HR operations, Talent Acquisition, Development and HR IT teams
Support reporting and analytics by providing data context, certified datasets, and guidance on usage
Ensure HR data handling adheres to privacy, security, and retention policies (e.g., GDPR, CCPA); coordinate with Legal, Privacy and Security teams
Deliver user training and change management on data-management and usage best practices and governance processes
Participate in AI/ML model governance lifecycle: intake, review, approval, monitoring, and decommissioning for HR models
Review datasets used for training/validation: confirm representativeness, label quality, feature provenance, and linkage to canonical HR records
Evaluate model inputs and outputs for appropriateness, interpretability, and the absence of impermissible/sensitive attributes unless explicitly justified and approved
Define and apply evaluation criteria and metrics relevant to HR (accuracy, calibration, precision/recall, and fairness measures such as demographic parity or equalized odds) and interpret their operational implications
Require and review documentation artifacts: model cards, data lineage, feature catalogs, version history, evaluation reports, and risk assessments
Ensure explainability, human-in-the-loop controls, and decision-review processes for models influencing hiring, promotion, compensation, performance management, or disciplinary actions
Monitor post-deployment performance and drift; trigger alerts, remediation, or retraining when thresholds are exceeded
Enforce data minimization and privacy-preserving techniques (de-identification, synthetic data, access controls) for model training and inference
Maintain or review audit trails for model predictions and decision logs to support investigations and compliance requests
Coordinate model risk reviews and approvals with Ethics/AI governance, Legal/Privacy, Security, and HR leadership
Educate HR stakeholders on model capabilities, limitations, appropriate use, and reporting obligations