Principal Cloud Data Engineer (Teradyne, North Reading, MA) Job Details | Teradyne

Teradyne is a global test and automation company that provides sophisticated solutions for manufacturing and next-generation technologies. The Principal Cloud Data Engineer will design, build, and operate multicloud data pipelines, models, integration frameworks, and AI-ready data products, primarily on Microsoft Azure. The role also focuses on data governance, quality, master data management, observability, technical leadership, and enabling enterprise AI platforms and analytics.

Responsibilities:

• Engineer end-to-end data solutions that directly drive AI insights and implementations, translating product requirements into scalable, production-grade data pipelines and data products
• Build the ingestion, transformation, and serving layers that ground enterprise AI platforms—Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, and Snowflake Cortex AI—in trusted proprietary data
• Develop embedding, vector, and retrieval pipelines that power RAG workflows and enable AI agents and applications to reason over enterprise data
• Enable insights through AI tooling by delivering curated, well-modeled datasets and semantic layers that fuel analytics, copilots, and AI-driven decision support
• Design, build, and optimize multicloud data solutions with Microsoft Azure as the primary platform, with an eye toward Google Cloud and AWS, applying consistent patterns across providers
• Put strong emphasis on the Microsoft Fabric ecosystem to unify data engineering and analytics workloads complimented with Snowflake
• Design, develop, and maintain scalable ELT/ETL pipelines that ingest and transform data from APIs, databases, files, SaaS applications, and streaming sources
• Implement modern lakehouse and medallion (bronze/silver/gold) architectures, and tune compute, storage, and access controls to balance performance, security, and cost
• Leverage Informatica and Fabric IQ to unify the implementation of data catalog, data lineage data quality, and application integration—including profiling, cleansing, validation across the data estate
• Advise on the design and maintenance of master data management solutions using Semarchy to ensure consistent, authoritative reference and master data across enterprise systems
• Apply “secure by default” access controls, data classification, and compliance safeguards to protect sensitive data used in AI and analytics
• Build automated testing, monitoring, and observability to track pipeline health, data freshness, quality, and cost, and proactively resolve data issues
• Partner with the Enterprise Architecture, product engineering, business, and operations teams to shape and deliver the data roadmap for AI products
• Establish reusable data engineering patterns, frameworks, and standards, and mentor engineers on modern multicloud and AI-enablement practices
• Evaluate emerging tools and technologies to keep Teradyne’s data platform modern, interoperable, and future-proof

Requirements:

• 8–10 years of experience in data engineering, with the last 2–3 years focused on modern cloud data engineering projects that enable AI products and analytics (cloud data warehouse/lakehouse, ELT, and pipeline automation)
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
• Strong proficiency in SQL and Python for data engineering and pipeline development
• Multicloud data engineering experience with Microsoft Azure as primary
• Strong, hands-on experience with the Microsoft Fabric ecosystem
• Experience engineering data solutions that enable AI products and insights through AI tooling (e.g., RAG/embedding pipelines, semantic layers) and integration with platforms such as Azure AI Foundry, Google Vertex AI, and Snowflake Cortex AI
• Strong proficiency in AI tooling such as Anthropic Claude or Microsoft CoPilot suite
• Hands-on experience with data governance, data quality, and application integration with tools such as Informatica
• Experience with Semarchy (or comparable) for master data management
• Experience with lakehouse/medallion and dimensional data modeling, plus CI/CD for data (Azure DevOps or GitHub Actions), testing, and observability
• Proven ability to mentor and upskill teams, fostering a culture of innovation and learning
• Strong collaboration and communication skills, with the ability to work across technical, product, and business teams
• Analytical mindset with a focus on delivering measurable business and AI outcomes