Senior Cloud Data Engineer: AI Data Pipelines & Multicloud

Teradyne; Reading (MA); Hybrid; USD 180,000 - 288,000; Full time

Job summary

Teradyne is seeking a Senior Cloud Data Engineer to design, build, and operate data pipelines and models powering enterprise AI solutions. You will work across Azure as the primary platform with multi-cloud integrations, ensuring high-quality, AI-ready data assets for analytics, copilots, and decision support.

The role emphasizes end-to-end data solutions, governance, and scalable lakehouse architectures, enabling AI-driven outcomes and cost-efficient data platforms across the enterprise.

Qualifications

• 8-10 years of experience in data engineering, with focus on cloud data projects for AI products.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• Strong proficiency in SQL and Python for data engineering and pipeline development.
• Multicloud data engineering experience with Azure as primary platform and Fabric ecosystem familiarity.
• Experience with data governance, data quality, and master data management tools.

Responsibilities

• Engineer end-to-end data solutions powering AI insights and analytics.
• Build ingestion, transformation, and serving layers for Azure, Google Cloud, and Snowflake ecosystems.
• Develop embedding, vector, and retrieval pipelines for RAG workflows.
• Deliver curated, well-modeled datasets and semantic layers for analytics and copilots.
• Mentor engineers and establish reusable data engineering patterns and standards.

Skills

SQL

Python

Azure

Multicloud

Data modeling

CI/CD

Education

Bachelor's degree in Computer Science

Tools

Informatica

Semarchy

Snowflake

Azure DevOps

GitHub Actions

Job description

Teradyne is seeking a Senior Cloud Data Engineer to design, build, and operate data pipelines and models powering enterprise AI solutions. You will work across Azure as the primary platform with multi-cloud integrations, ensuring high-quality, AI-ready data assets for analytics, copilots, and decision support.

The role emphasizes end-to-end data solutions, governance, and scalable lakehouse architectures, enabling AI-driven outcomes and cost-efficient data platforms across the enterprise.