Principal Data Engineer

Applied Systems is seeking a Principal Data Engineer to shape the architectural foundation of its data and analytics capabilities. The role focuses on large-scale data architecture, AI-enabled data workflows, governance, observability, modeling standards, and mentoring engineering teams.

Responsibilities:

• Define large-scale data architecture decisions — batch/streaming platforms, warehouses, and lakehouses — and evaluate tradeoffs for quality, scalability, and long-term sustainability
• Architect observability, alerting, and incident-response frameworks to monitor the health and SLAs of data pipelines across the organization; serve as the technical escalation point for complex data architecture problems
• Partner across Applied teams to define standards for data quality, lineage tracking, access control, cataloging, and governance; establish SLAs/SLOs for critical data assets
• Design data modeling standards across OLAP/warehouse (dimensional modeling), OLTP/transactional, streaming/real-time, and AI/vector/feature-store use cases
• Define patterns for embedding AI and agent-supported workflows across the data engineering lifecycle, and enable teams to adopt them at scale
• Define and enforce data engineering standards, best practices, and design patterns across the organization
• Mentor and grow engineers through code reviews, design discussions, and technical guidance
• Influence cross-functional roadmaps by translating business requirements into sound data architecture strategies

Requirements:

• 15+ years in software/data engineering or architecture with outstanding impact
• Degree in Computer Science, a related field, or equivalent combination of education and experience
• Expert knowledge of data architecture principles and best practices
• Hands-on builder experience (actively coding, testing, and validating architecture at scale)
• Proven ability to influence without authority
• Pragmatic approach and obsessed with eliminating waste
• Player-coach mindset with a commitment to teaching through action
• Adaptability, resilience, and the ability to thrive in ambiguity with iteration
• Excellent communication skills, able to meet your audience where they are and explain complex problems clearly
• Proven experience architecting large-scale data warehouses or lakehouses
• Expertise across data and software technologies — GCP BigQuery and dbt a plus
• Solid understanding of data modeling concepts including dimensional modeling and streaming architectures; experience with vector stores is a plus
• Strong understanding of data governance; experience implementing data catalogs a plus