Senior Business Intelligence and Governance Architect

Senior Business Intelligence and Governance Architect Company: The Boeing Company Location options include Chicago, IL; Seattle, WA; Arlington, VA; North Charleston, SC; 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. Position 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 Basic Qualifications (Required Skills/Experience) 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) Preferred Qualifications (Desired Skills/Experience) Bachelor’s degree in Human Resources, Information Management, Data Science, Business, or related field (or equivalent experience) Experience interpreting model and fairness metrics and translating 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 Compensation & Benefits Pay is based upon candidate experience and qualifications, as well as market and business considerations. Summary pay range: $149,600 – $216,200. Benefits include competitive base pay, variable compensation, health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance, and paid/unpaid time off. Specific programs and options may vary by location and eligibility factors. Other Information This position is 100% onsite at one of the listed locations and requires a U.S. Person for export control compliance. It does not require a security clearance or visa sponsorship. Equal Opportunity Employment Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other protected characteristics. Boeing EEO Policy. Request an accommodation. #J-18808-Ljbffr Boeing