Principal Cloud Data Engineer (Teradyne, North Reading, MA)

Teradyne•North Reading, MA

6d•$179,700 - $287,500•Onsite

About The Position

As a Senior Cloud Data Engineer at Teradyne, you will engineer the data solutions that power our enterprise AI implementations. Reporting to the Enterprise AI/Data Product Manager, your primary mission is to design, build, and operate the pipelines, models, and integration frameworks that turn Teradyne’s operational and product data into trusted, governed, and AI-ready data products—the foundation on which our AI solutions are built and scaled. This is a deeply technical, delivery-focused role centered on enabling AI outcomes. You will build multi-cloud data solutions with Microsoft Azure as our primary platform. You will engineer the ingestion, transformation, and serving layers that ground our AI platforms—including Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, and Snowflake Cortex AI—in high-quality proprietary data, while leveraging Informatica for data governance, data quality, and application integration, and Semarchy for master data management. Your work directly enables business, operations, and product engineering teams to unlock insights through modern AI tooling.

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.

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.