Engineering Manager (Autonomy Storage Infrastructure)

Applied Intuition; Sunnyvale (CA); On-site; USD 210,000 - 320,000; Full time

Job summary

Applied Intuition is seeking an Engineering Manager to lead the Autonomy Storage Infra team within the Data & Compute org. You will manage a world-class data engineering team responsible for the full data lifecycle—from collection and ingestion to storage, querying, and retrieval—at hundreds of PBs scale.

You'll partner across business units to define the technical roadmap for storage infra, ensuring support for external products and internal tooling across cloud, hybrid, and on-prem

Qualifications

• 6+ years of professional software engineering experience, with 2+ years in an engineering management or tech lead role.
• - Bachelor’s degree in Computer Science, Software Engineering, or equivalent.
• - Hands-on experience with large-scale open-source data technologies (Spark, Hudi/Iceberg, LanceDB, dbt, etc.).
• - Strong cross-functional communication skills and the ability to influence technical direction at a senior level.
• - Experience managing engineers across the full data lifecycle in a production environment.
• - Multi-cloud or multi-region data platforms experience.
• - Streaming and change data capture technologies such as Kafka, Pub/Sub, Flink, or Debezium.

Responsibilities

• Lead, grow, and mentor a team of data infrastructure engineers, fostering a culture of technical excellence, ownership, and collaboration.

Skills

Spark

Hudi/Iceberg

LanceDB

dbt

Kafka

Pub/Sub

Cloud platforms

Cross-functional leadership

Multi-cloud / multi-region

Data governance

Observability

Education

Bachelor’s degree in Computer Science, Software Engineering, or equivalent

Tools

Docker

Kubernetes

Airflow

Job description

• We are looking for an Engineering Manager to lead our Autonomy Storage Infra team within the Data & Compute org. In this role, you will manage a world class data engineering team responsible for the full data lifecycle - from collection and ingestion to storage, querying, and retrieval - at hundreds of PBs scale
• You will partner closely with all business units across Applied Intuition to define the technical roadmap for our storage infra and ensure we can reliably support both external products and internal tooling across cloud, hybrid, and on-prem deployments
• This is a highly cross-functional leadership role that requires deep technical credibility, strong people management skills, and a bias for ownership. At Applied Intuition, engineering managers are expected to be hands‑on leaders who actively engage in technical direction, not just people management
• Lead, grow, and mentor a team of data infrastructure engineers, fostering a culture of technical excellence, ownership, and collaboration
• Define and drive the technical roadmap for storage infrastructure, balancing short‑term delivery with long‑term scalability
• Own the architecture and reliability of systems that handle the full data lifecycle across all deployment environments (cloud, hybrid, on‑prem) and regions
• Drive engineering decisions around scalable data ingestion, indexing, storage, and retrieval for high‑volume autonomous vehicle and physical AI workloads
• Establish and uphold engineering best practices, code quality standards, and operational excellence across the team
• Recruit top engineering talent and build an inclusive, high‑performing team

Requirements

• Hands‑on experience with large‑scale open‑source data technologies (Spark, Hudi/Iceberg, LanceDB, dbt, etc.)
• Strong cross‑functional communication skills and the ability to influence technical direction at a senior level
• Experience managing engineers across the full data lifecycle in a production environment
• 6+ years of professional software engineering experience, with 2+ years in an engineering management or tech lead role
• A Bachelor’s degree in Computer Science, Software Engineering, or equivalent
• Deep understanding of containerization, cloud‑native architectures, and modern software development workflows
• A strong track record of leading teams that build and operate high‑scale distributed data systems
• Multi‑cloud (GCP, AWS, Azure) or multi‑region data platforms, including data‑residency requirements
• Streaming and change data capture technologies such as Kafka, Pub/Sub, Flink, or Debezium, ideally including running them at high scale
• Familiarity with data quality, data profiling, and data governance frameworks
• Building and operating observability or monitoring for data systems at scale
• Working in high‑growth environments where data infrastructure had to evolve rapidly to keep pace with the business
• Prior exposure to automotive, defense, robotics, or other physical AI domains