Principal Cloud Data Engineer

Principal Cloud Data Engineer

Opportunity Overview

• Engineering Data Solutions for AI Products

• 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.

Business Outcome: Accelerate delivery of AI solutions by ensuring high-quality, AI-ready data is available, discoverable, and trusted across the enterprise.

• Multicloud Data Platform & Pipeline Engineering

• 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.

Business Outcome: Provide a scalable, cost-efficient, and resilient multicloud data platform that supports enterprise AI, analytics, and operational use cases.

• Data Governance, Quality & Master Data Management

• 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.

Business Outcome: Enhance data trust, governance, and reliability—reducing risk and improving the accuracy of AI and analytics as measured by data quality and governance metrics.

• Collaboration & Technical Leadership

• 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.

Business Outcome: Raise the capability of the data organization and ensure data solutions are tightly aligned with enterprise AI and product strategy.

All About You

• We seek individuals who share our passion and determination. Our commitment to customer success drives us to go the extra mile. If you’re ready to join us in this mission, take a closer look at the minimum criteria for the position.

• 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