Aerospace Data Engineer - Real-time Pipelines

Archer; San Jose (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Archer, an aerospace company in San Jose, CA, seeks an experienced Data Engineer to specialize in aviation data and analytics for scalable flight test data processing and real-time aircraft health analysis.

You will design and optimize data processing systems, ETL pipelines, and data models, while ensuring data quality and governance. Collaboration with analytics and engineering teams is essential to enable rapid design iterations and predictive maintenance.

Qualifications

• Experience with SQL and programming languages (Python, Go, Rust).
• Designing and managing data warehouse/lakehouse architectures for high performance and low latency.
• Led development of real-time data pipelines using Kafka or related technologies.
• Expertise with one or more cloud data platforms (AWS, GCP, Azure).
• Knowledge of data security and quality governance.

Responsibilities

• Architect, build, and optimize data processing systems for reliability and scalability.
• Develop and optimize ETL processes, data models, and architectures.
• Maintain data models that support analytics, reporting, and app development.
• Create data quality observability, monitoring, testing, and validation frameworks.
• Collaborate with analytics teams to meet data needs for decision making.
• Integrate data systems with aircraft telemetry and flight testing workflows.

Skills

SQL

Python

Go

Rust

Data modeling

Education

Bachelor’s or Master’s degree in Computer Science, Aerospace Engineering, Data Analytics, or related field

Tools

Kafka

Docker

Kubernetes

Terraform

Cloud platforms (AWS/GCP/AZURE)

Job description

Archer, an aerospace company in San Jose, CA, seeks an experienced Data Engineer to specialize in aviation data and analytics for scalable flight test data processing and real-time aircraft health analysis.

You will design and optimize data processing systems, ETL pipelines, and data models, while ensuring data quality and governance. Collaboration with analytics and engineering teams is essential to enable rapid design iterations and predictive maintenance.