Junior Data Engineer

We're looking for a Junior Data Engineer to help us scale the leading GPU data platform.

Who We Are

At Silicon Data, we bring transparency to the GPU economy by delivering real-time pricing intelligence, market analytics, and sustainability insights for compute infrastructure. Operating at the intersection of AI, finance, and infrastructure, our product suite—including GPU index products, benchmarking tools, and forward pricing modules—helps organizations benchmark GPU performance, predict cost trends, and measure compute efficiency. You can learn more at https://www.silicondata.com/. This role is part of our data team, supporting the pipelines that power our GPU pricing intelligence and benchmarking products, where you'll grow quickly as we scale.

What You'll Do

• Build and maintain ETL/ELT pipelines that clean, transform, and load GPU pricing and benchmarking data
• Write SQL and Python to process and validate large datasets
• Monitor pipeline health and troubleshoot data quality issues
• Collaborate with senior engineers on schema design and pipeline architecture
• Collaborate with analytics, data science, and engineering teams to accelerate adoption of platform capabilities, uphold data governance standards, and deliver on shared roadmap commitments.

Required Qualifications

• 0-2 years of experience in data engineering, software engineering, or a related internship
• Solid proficiency in SQL and Python
• Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
• Comfort working with large, real-world datasets
• Willingness to learn quickly and take ownership of pipeline components

Preferred Qualifications

• Familiarity with pipeline tools such as Airflow or dbt
• Experience with cloud data warehouses (Iceberg, Deltalake)
• Familiarity with Git and Docker
• Interest in GPU infrastructure, pricing data, or compute markets

Why You'll Love Working Here

• Small, senior team with fast feedback loops and high autonomy
• Work on technically challenging problems relevant in the AI economy
• Fully remote