Manager, Data Engineering

Manager Of Data Engineering

Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like  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.

An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota's vision to move people beyond what's possible. At TFS, you will help create best-in-class customer experience in an innovative, collaborative environment.

Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future.

This position is an onsite role based in Plano, TX.

Toyota Financial Services is on a mission to modernize how data is sourced, stored, and used across our global ecosystem. We're looking for a strategic and hands-on Manager of Data Engineering to lead the vision, design, and implementation of our enterprise-wide data acquisition and ingestion platform—powering everything from analytics to AI and Data Products in a Business Domain.

What you'll be doing:

• Lead the design, and execution of a modern, scalable data acquisition and analytical platform to ingest data from internal systems, APIs, microservices, and third-party sources.
• Design, Build robust production-grade pipelines and self-service tools that enable software and data engineers to acquire data at scale—securely and efficiently.
• Architect cloud-native lake and lakehouse solutions (AWS, Snowflake) and implement multi-tenant capabilities to serve global Toyota entities.
• Partner across business, product, data architecture, and platform teams to deliver an integrated, governed, and discoverable data ecosystem.
• Define and champion data engineering best practices: data modeling, schema evolution, data contracts, testing strategies, lineage tracking, cataloging, and governance
• Proactively communicate technical risks, tradeoffs, and recommendations to both engineering and non-technical stakeholders
• Collaborate closely with Engineering Managers, Product, Data Science, and Analytics to shape data roadmaps and ensure the platform evolves with business needs
• Design and implement data quality frameworks—validation rules, anomaly detection, freshness checks, and alerting—so downstream consumers can trust the data without asking
• Identify and resolve systemic data issues: pipeline failures, data quality degradation, schema drift, latency in streaming systems, cost inefficiencies in storage and computing, and gaps in data observability
• Lead and grow a high-performing team of 6+ engineers, setting a high bar for technical depth, delivery, and innovation.

What you bring:

• 10–15+ years in data engineering and platform tech leadership roles.
• Hands-on experience with enterprise-scale data acquisition methods (CDC, APIs, file transfer, event streaming).
• Deep knowledge of cloud-based lake/lakehouse architectures (AWS, Redshift, Snowflake, or similar).
• Strong programming skills (Python or Java) and experience with microservices and APIs.
• Proven ability to design and implement scalable, secure, and governed data platforms.
• Exceptional communication skills, with the ability to align business and technical stakeholders.

Added bonus if you have:

• Advanced degree in Computer Science, Engineering, or related field.
• AWS Solutions Architect certification or higher.
• Experience building data catalogs, managing data privacy, and implementing governance frameworks.
• Exposure to big data ecosystems (Hadoop, Spark, Hive, etc.).
• Experience working in…