Principal AI Data Readiness Architect

Safety and security are at the heart of everything we do at Motorola Solutions. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations.

• 8+ years in data engineering/architecture/platform roles, preferably >1 year at Staff/Principal level.
• Expert SQL and Python; track record building enterprise data governance, contracts, and quality frameworks.
• Experience operating production data platforms in batch/near-real-time with strong lineage and access control.
• Practical unstructured data governance (metadata standards, classification, PII detection/redaction).
• Hands-on with catalogs/lineage as systems of record for definitions, ownership, and policy.
• Familiarity with vector/RAG readiness concepts (schemas, metadata, provenance) without owning embeddings/model development.
• Experience with workflow orchestration (e.g. Airflow) and CI/CD/testing for data pipelines.
• English language proficiency at advanced level.

• Define the enterprise AI data architecture vision, principles, and reference architectures.
• Lead cross-functional reviews with IT, security, legal/privacy, and business stakeholders to align on data readiness roadmaps.
• Establish data contracts for AI consumption (schemas, semantics, classifications, SLAs) and govern schema evolution for backward compatibility.
• Make the data catalog the system of record for lineage, ownership, definitions, and policy labels; integrate with intake/change management.
• Define standard data models and semantic conventions that improve joinability and reuse across domains.
• Implement an enterprise data quality framework and automated scorecards (freshness, completeness, accuracy, consistency).
• Monitor for anomalies and schema drift; publish AI data readiness dashboards (catalog coverage, lineage depth, PII detection coverage, contract adherence).
• Standardize patterns for ingestion, processing, storage, serving, and environment promotion using Airflow or other standard ETL/Orchestration tools and CI/CD for data workflows.
• Define secure, consistent access patterns/APIs for downstream analytics and AI consumers.