Principal Data Engineering Architect

Business Understanding & Impact Partner with PMs and business users to define problems, connect requests to customer journeys and business outcomes, and prioritize reusable capabilities. Use feedback, adoption, reuse and quality measures to guide product decisions and team data strategy. Lead solution design and production implementation of major data capabilities. Build hands-on prototypes, contribute production code, review code, models and designs, and remain accountable for operational reliability and measurable product outcomes. Develop logical, physical and semantic models for foundational marketing domains. Productize engagement signals with clear business definitions, metric logic, contracts and ownership so analysts, applications and AI consumers can use data without guessing its meaning. Design distributed processing and storage using streaming, batch or combined patterns. Apply immutable event data and flexible schema evolution to protect downstream products. Make explicit tradeoffs among latency, accuracy, reliability, cost and maintainability. Influence engineering and user-facing tools for governed self-service ingestion, publishing and extension of platform data. Improve discovery, access and user experience through interoperable templates, interfaces and semantic abstractions that preserve domain ownership. Embed security, privacy and quality by design, including metadata, lineage, classification, policy-aware access and retention. Establish monitoring and engineering practices that make federated products trustworthy, supportable and cost-conscious throughout their lifecycle. Advance AI automation and governed code generation for transformations, tests, documentation and self-service data extension. Use bounded assistance for operational detection and recommendations, with rigorous validation, approved remediation and human judgment for consequential changes. Measure delivery and operational improvements. Bring a startup mindset to a large, complex data environment: listen, simplify, iterate quickly and make sound decisions. Mentor and coach engineers, strengthen reviews and standards, and align partner teams through technical credibility and collaboration. Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling, or data engineering Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 8+ years experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 12+ years experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience. 3+ years experience with data governance, data compliance and/or data security. 8+ years of experience across the software development lifecycle, including 2+ years in a technical lead role designing and delivering large-scale data platforms. Demonstrated expertise in distributed and big-data processing, streaming and batch systems, logical and physical data modeling, semantic abstraction and schema evolution; proficiency in SQL and a programming language. Proficient business acumen and product judgment, with demonstrated ability to understand business users, translate ambiguous needs into reusable data capabilities, and connect engineering decisions to customer and business outcomes. Demonstrated cross-functional technical leadership, mentoring and influence through partnership. Clear written, verbal and presentation communication for technical and business audiences, and sound judgment informed by quantitative and qualitative evidence in fast-moving, ambiguous environments. Experience with marketing, digital media or web analytics, customer journey measurement, and data products supporting analytics, data science and activation. Experience building federated self-service platforms or governed AI-assisted engineering and discovery tools, with evidence of improved adoption, delivery efficiency or operational quality.