Principal AI Data Readiness Architect

Motorola Solutions Systems Polska is seeking a Staff/Principal AI Data Architect to modernize their enterprise data ecosystem to support new AI and ML tools. The role focuses on data readiness, governance, quality, and secure access within an enterprise environment.

• 8+ years in data engineering/architecture/platform roles, preferably >1 year at Staff/Principal level.
• Expert in 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 experience with unstructured data governance (metadata standards, classification, PII detection/redaction).
• Hands-on experience 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.

• 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.
• Drive foundational architecture and standards to enable advanced Retrieval Augmented Generation (RAG) and semantic search capabilities.
• Provide guidance for chunking/segmentation policies, deduplication, and hybrid search compatibility; downstream teams implement embeddings/vector stores.
• Define safe-access patterns for AI consumption to prevent sensitive data exposure.
• Enforce security baselines (encryption, RBAC/ABAC, masking/tokenization) and policy-as-code for access.
• Architect for transparent cost attribution and controls (tagging, storage tiering, retention) to enable informed cost/performance choices by consumers.