Data Quality & Pipeline Architect

We are seeking a Staff or Principal level Data Architecture Lead within the centralized IT Data Infrastructure team. This is a high-impact, high-visibility role, central to modernizing our data warehouse infrastructure and ecosystem for 2026 and beyond. You will be a key driver in ensuring our stakeholders have high confidence in the reliability, timeliness, and accuracy of our data, ultimately helping Motorola unlock our data to become fully AI and LLM ready.

Responsibilities:

Leadership & Strategy

• 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.

Data Quality Architecture

• 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.

Pipeline Architecture and Orchestration

• 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.

Reliability & Observability

• 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 (unit, integration, validation) and environment promotion.

Data Platform Design & Optimization

• Design end-to-end data platform patterns across ingestion, processing, storage, serving, observability, and governance for batch and streaming.

• Document trade-offs and decisions for tooling, formats, and processes; ensure reproducible, auditable standards.

• Define and enforce data freshness, completeness, accuracy, and reliability objectives; establish error budgets and escalation pathways.

• Design and enforce data security standards, including encryption strategies, access control mechanisms (RBAC/ABAC), and tokenization/masking techniques for sensitive data.

• Define clear API and interface standards for how upstream source systems and downstream consumption tools (BI, Analytics, ML) interact with the data platform.

• Forecast and optimize compute/storage costs, throughput, and latency; build capacity plans and efficiency goals.

Data Modeling and Governance

• Write data governance standards and make recommendations for implementation.

• Define standard data models and principles.

• Ensuring the data catalog (e.g., Alation) is fully integrated into the data lifecycle and serves as the single source of truth for lineage, definitions, and ownership.

Mentorship and Collaboration

• Create reference implementations and mentor junior engineers in their development and design of proposed solutions.

• Partner effectively with business stakeholders to define compliance SLA requirements, data contracts, and acceptance criteria.

Testing, CI/CD & DevOps

• Automate testing for data: unit, integration, validation tests, and regression checks; fixture design and synthetic test data.

• Implement CI/CD pipelines for data workflows: Git-based development, code reviews, and environment promotion.

Basic Requirements

Required Qualifications

• 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.