Senior AI Data Engineer — Scalable Data Pipelines

Iconcareers; Austin (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

ICON is seeking an entrepreneurial Senior AI Data Engineer I to design, build, and maintain data infrastructure powering our data-driven products and services. You will collaborate with AI engineers, data labelers and external data providers to assemble large, high-quality datasets.

You will design robust data pipelines, scalable storage, and modeling approaches to enable ML workflows, data labeling, and rapid iteration in a fast-paced environment in Austin, Texas.

Qualifications

• 8+ years of software development experience.
• 6+ years of experience managing data.
• Proficient in Python and cloud data services.
• Hands-on experience with data storage technologies.
• Degree in Computer Science, related technical field or similar experience.
• Strong problem solving skills and ability to work independently.

Responsibilities

• Design workflows, tools and services for managing large datasets.
• Develop and maintain large, high quality datasets.
• Find new sources of data.
• Build infrastructure to support data labeling, data review and machine learning.
• Design and implement robust data pipelines to ingest, transform, and load data from a variety of sources.
• Build scalable data storage and processing solutions.
• Develop efficient algorithms and data models to help curate data and maintain high quality.

Skills

Python

Cloud data services

Problem solving

Independent work

Data management

Education

Bachelor's degree in Computer Science or related field

Tools

AWS Cloud Development Kit

GitHub

Jenkins

Travis CI

Job description

ICON is seeking an entrepreneurial Senior AI Data Engineer I to design, build, and maintain data infrastructure powering our data-driven products and services. You will collaborate with AI engineers, data labelers and external data providers to assemble large, high-quality datasets.

You will design robust data pipelines, scalable storage, and modeling approaches to enable ML workflows, data labeling, and rapid iteration in a fast-paced environment in Austin, Texas.