Data Quality & Pipeline Architect

Motorola Solutions Systems Polska is seeking a Staff or Principal level Data Architecture Lead within the centralized IT Data Infrastructure team to modernize their data warehouse infrastructure and ecosystem for 2026 and beyond.

• 8+ years of progressive experience in data engineering or analytics platform roles, with 2+ years in a dedicated Data Architecture, Principal, or Staff role.
• Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, or a related field; or equivalent practical experience.
• Demonstrated leadership designing and rolling out enterprise data quality and pipeline standards at scale.
• Expert proficiency in SQL and Python (or a similar major language like Scala/Rust), with the ability to review and guide code quality and performance.
• Proven experience operating both batch and streaming data systems in production, with end-to-end accountability for reliability and data trust.

• Define the long-term vision, standards, and reference architectures to drive modern data warehouse implementation.
• Lead cross-functional design reviews to influence roadmaps and platform evolution.
• Establish and report on Service Level Objectives (SLOs) and Service Level Agreements (SLAs) for platform performance and adoption.
• Design and roll out enterprise-wide data quality and pipeline standards at scale, including creation of formal data contracts for schema evolution.
• Architect the data quality framework (test specification, governance, and lifecycle) and establish automated scorecards for completeness, accuracy, and consistency.
• Develop a statistical-based approach for monitoring data quality shifts, volume anomalies, and identifying outliers.
• Define standard data models and principles, and ensure the data catalog (e.g., Alation) serves as the single source of truth for lineage and ownership.
• Design end-to-end data platform patterns across ingestion, processing, storage, serving, observability, and governance for batch and streaming.
• Establish governance around data loading (e.g., Airflow DAGs) to ensure data integrity and enable rollback capabilities.
• Define clear API and interface standards for how upstream source systems and downstream consumption tools (BI, Analytics, ML) interact with the data platform.
• Design and enforce data security standards, including encryption strategies and access control mechanisms (RBAC/ABAC) for sensitive data.
• Forecast and optimize compute/storage costs, throughput, and latency; build capacity plans and efficiency goals.
• Establish robust data monitoring (freshness, volume, schema drift) with automated alerting for violations.
• Build for platform resilience, including data loading retry with backoff, checkpointing, and a disaster recovery plan.
• Lead incident response, conduct post-mortems, and update policies to prevent similar future incidents.
• Implement CI/CD pipelines for data workflows, including automated testing.