Data Engineer Jobs in Santa Clara, CA

Design, build, and maintain cloud-based engineering data platforms and production pipelines. Create trusted analytics datasets, data models, validation rules, and semantic layers to support engineering, reporting, and AI-enabled insights. Partner cross-functionally to optimize performance, correctness, scalability, and cost for large-scale semiconductor test, manufacturing, and engineering data.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze structured and unstructured data to support analytics, ML, and reporting. Implement streaming solutions, workflow orchestration, and ensure reliable, performant, and cost-effective production data platforms.

Design, build, and maintain scalable data lake and warehouse infrastructure for large-scale autonomous driving data. Identify and resolve edge cases by analyzing field data, collaborate with ML and annotation teams, monitor metrics, manage data quality, schemas, access controls, and optimize ETL/ELT and processing pipelines to improve system performance and edge-case resolution workflows.

Build and operate scalable backend services and data platform integrations for customer profiles, consent, personalization, campaign operations, and marketing intelligence. Design APIs, service integrations, and reusable platform capabilities; modernize database and identity systems; ensure reliability with observability, automation, and incident prevention; partner with cross-functional teams to translate business needs into production-ready solutions using AI-assisted engineering responsibly.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

The Senior Software Engineer will scale open-source data infrastructure, handle data lifecycle tasks, and support internal and external products.

The Sr Staff Engineer will architect and manage the data infrastructure, ensuring reliability and performance for data engineering and machine learning teams, while automating processes and monitoring systems.

Design and operate petabyte-scale end-to-end data pipelines for autonomous driving: onboard upload, cloud preprocessing, dataset production, and model training input. Build data cleaning, annotation QA, lineage, versioning, metadata management, and high-throughput distributed processing. Optimize I/O, memory, transmission, and containerized deployments; support cross-team collaboration and algorithm iteration.

Build and productionize internal AI-powered tools and data systems. Implement and maintain infrastructure, data pipelines, deployments, monitoring, and access controls. Integrate third-party AI services, ensure data quality, document systems, and collaborate with stakeholders to deliver maintainable solutions.

As a Cloud Data Engineer, you'll build and maintain data pipelines, evaluate new technologies, and enhance AI outcomes. You'll collaborate with data scientists and develop applications in cloud environments using big data technologies.

The Principal Software Engineer will oversee technical readiness for NVIDIA's GT data generation, resolving upstream blockers and ensuring quality across sensors and data platforms while collaborating with teams in the US and China.

Lead design and implementation of high-performance data plane software for DPU-based Smart Switches. Write and optimize C/C++ on Linux, build packet-processing and networking features, troubleshoot complex networking and customer issues, participate in architecture and code reviews, and mentor engineering teams to deliver scalable, reliable systems.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Design, build, and maintain scalable data pipelines and ETL for real-time marketing applications. Collaborate with product and analytics teams, ensure data quality, reliability, and performance, troubleshoot issues, and contribute to design and code reviews and documentation.

Design and operate distributed inference systems for LLMs, build large-scale data pipelines, and debug production issues while collaborating with researchers and ML engineers.

Design and implement real-time lane-fusion algorithms that combine multi-source perception and temporal data to produce stable 3D lane and road representations. Research solutions for occlusion, fragmentation, forks/merges, and construction zones. Build a 3D laneline pipeline with camera perception, optimize for low latency and high availability on safety-critical vehicle platforms, and collaborate with cross-functional teams to deploy on target systems.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

Design and implement cloud-native data management services (catalog, metadata, dataset/checkpoint lifecycle) for exabyte-scale GPU workloads. Build backend systems on Kubernetes and major cloud providers, collaborate with research and cross-functional teams, document architecture, and drive integration with storage/compute innovations (GPU Direct Storage, DPU).

Design, build, and own scalable batch and streaming data pipelines and data models for analytics and ML. Collaborate with data science, engineering, and product to deliver data products and dashboards, enforce data quality and testing, mentor engineers, and set technical direction for the data platform.

Lead architecture and development of data-intensive services and APIs for robotic platforms. Design ingestion and processing pipelines, define interfaces and data contracts, implement comprehensive testing and CI/CD, drive observability and operational excellence, collaborate cross-functionally, ensure security and compliance, and mentor engineering teams.

Lead design, build, and maintenance of scalable data pipelines, sandbox databases, and analytics dashboards. Investigate and resolve complex data discrepancies, perform ETL/ELT, data modeling, and performance tuning using Databricks, Starburst, Kafka, SQL, and Python. Partner cross-functionally, enforce data governance, document processes, and mentor team members to support research prototyping and business insights.

Design, build, and scale open-source data infrastructure across collection, ingestion, storage, querying, and retrieval. Improve ingestion pipelines, expose performant query interfaces and integration hooks, and deploy reliable data platform features across cloud, hybrid, and on‑prem environments.

Design and support robotic and data-collection hardware, build large-scale robotic simulation environments for RL training, process human motion data, collaborate on research publications, and integrate algorithms with ADAS, data, and simulation engineering teams.

Build and maintain large-scale ETL and ML infrastructure to automate data selection, labeling, training, and testing loops. Connect real-world and simulation data to enable continuous model improvement, collaborate with modeling teams, and mentor junior engineers to scale data-centric development.