Principal Engineer 2, Data Engineering - Data Engineering

Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We’re looking for talented team members who want to Dream. Do. Grow. with us.

What you'll be doing

• Define and advance the technical vision for large-scale supply chain data platforms built on AWS, Databricks, and Azure, ensuring alignment with business priorities, enterprise architecture, and long-term scalability objectives.
• Lead architecture and design decisions across data products and development teams, balancing speed, quality, resiliency, security, maintainability, and cost optimization.
• Provide principal-level technical leadership across the full data solution lifecycle, from discovery and architecture through implementation, deployment, and operational support for big data and AI/ML workloads.
• Mentor data engineers, technical leads, and engineering managers to elevate technical excellence and strengthen engineering capability across the organization.
• When needed, directly lead multiple development teams by setting priorities, coordinating execution, and establishing effective delivery rhythms across parallel workstreams.
• Drive disciplined execution through scaled agile delivery practices — clear user stories, defined acceptance criteria, effective dependency management, risk mitigation, and measurable outcomes across the development lifecycle.
• Work side by side with product, operations, architecture, and platform partners to shape strategic priorities into actionable technical roadmaps and execution plans for cloud data, analytics, and AI/ML solutions.
• Lead rigorous technical discussions and challenge design decisions to ensure solutions meet business objectives, technical feasibility, data governance, and operational excellence standards.
• Establish and champion modern engineering practices — cloud-native architectures, DevSecOps automation, observability, reliability engineering, and high-quality software delivery standards.
• Identify cross-team dependencies and organizational bottlenecks, and drive alignment and decision-making across engineering teams and stakeholder groups.
• Provide senior leadership with clear visibility into technical direction, delivery health, risks, tradeoffs, and key decisions.
• Set the standard for AI-assisted engineering: use AI as an assistant in your own work, and define where and how teams apply it to improve efficiency, quality, and outcomes — with clear expectations for review, security, and code quality.
• Decide what is worth testing and create the room for the team to test it — running time-boxed experiments and prototypes against architecture, tooling, and emerging technology, and turning what proves out into standards.
• Stay close to the operation. Spend time with the supply chain teams who depend on these platforms so architecture and design decisions reflect how the work actually runs.
• Champion a culture of accountability, curiosity, experimentation, continuous learning, technical excellence, and inclusive leadership across the engineering organization.

What you bring

• 8+ years designing and delivering complex enterprise data and software solutions, including large-scale transformation initiatives.
• 5+ years of deep experience with cloud-native data architecture on AWS, Databricks, and Azure, distributed systems, modern integration patterns, and scalable platform design.
• 5+ years delivering modern data solutions using technologies such as Python, PySpark, SQL, Databricks, Apache Spark, AWS Redshift, and streaming frameworks, with contemporary DevOps and platform-engineering practices.
• Demonstrated success setting technical direction, influencing across multiple teams, and partnering effectively with engineering, product, architecture, and business stakeholders.
• Ability to balance technical excellence, delivery outcomes, AI-enabled engineering capabilities, and cost optimization in a complex enterprise environment.
• Demonstrated use of AI as an assistant in your own engineering work, and experience raising that capability across a team — with the judgment to know where it helps, where it does not, and how to hold quality, security, and review standards.
• A track record of digging to root cause on hard, ambiguous problems, and of understanding the business problem before reaching for a solution.
• Experience leading in a cross-functional team model where engineering and operations share one goal, and comfort re-forming teams as priorities move.

Technical Expertise

• Expertise in data architecture, distributed data processing, domain-driven design, and technical decision-making for scalable data platforms.
• Ability to lead complex design discussions spanning cloud-native data architectures, streaming and event-driven systems (Kinesis, Kafka/MSK), data governance, security, observability, reliability, and operational excellence.
• Experience designing and implementing AI/ML solutions — data preparation, model integration, inference workflows, and automation use cases — with familiarity with AWS AI/ML services such as SageMaker and Bedrock.
• Demonstrated ability to incorporate cost-optimization principles into architecture, cloud platform decisions, and engineering delivery.
• Experience integrating data, analytics, and AI/ML capabilities into production-grade enterprise platforms.

Leadership Capabilities

• Experience leading or coordinating multiple software development teams across complex enterprise initiatives.
• Demonstrated ability to mentor senior engineers and technical leads, influence without authority, and step into direct leadership when needed.
• Strong experience aligning roadmaps, managing dependencies, and driving execution across cross-functional stakeholders and shared priorities.
• Ability to lead adoption of AI-enabled delivery capabilities across teams with appropriate standards, governance, and enablement.
• Excellent communication skills, with the ability to articulate technical strategy, tradeoffs, and delivery status to technical and non-technical audiences.