Data Engineer Jobs in Sunnyvale, CA

Architects data models and pipelines that transform multimodal surgical and robotic data into research-ready AI datasets. Owns data collection strategy, quality frameworks, validation, anomaly detection, lineage, annotation workflows, and dataset tooling. Partners with AI researchers, surgeons, clinicians, and robotics engineers to capture relevant signals and support intelligent surgical system development. Contributes to embodied AI infrastructure and data strategy.

Design, build, and modernize full‑stack web applications and microservices for a real‑time event-driven data and visualization platform. Develop and optimize streaming data pipelines (Kafka, Flink, Spark), integrate observability, caching (Redis), and AI agents, and collaborate with hardware verification teams for scalable, low-latency solutions.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Build and manage high-volume analytics and production ETL/ELT systems, ensuring reliable data ingestion, quality, observability, security, compliance, and scalable performance. Partner with Data Science, Engineering, and Product teams to support accurate Search data logging and usage. Define data-system SLAs, optimize cost and performance, and support massive datasets, experimentation, and machine-learning applications.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

The Big Data Developer (Spark) will be responsible for Spark and Scala development, Hadoop performance tuning, and SQL coding. Requires extensive experience in data processing and performance optimization.

Lead architecture and implementation of scalable ML data pipelines and tooling to turn autonomy data into training datasets. Build data selection, active learning, inference-at-scale, vector DB management, labeling integration, QC automation, and introspection tools while partnering with ML researchers and infra teams.

As a Principal Data Processing Engineer, you will enhance a data processing engine, collaborate on open-source platforms, and optimize performance.

As a Senior Data Engineer at TRI, you will design and build robust data infrastructure and pipelines to support autonomy research and simulation workflows.

Own cloud software and data platform services end to end, including architecture, APIs, data models, pipelines, production operations, monitoring, incident response, and reliability. Build scalable systems for sensor-generated data, use AI-assisted development tools, mentor engineers, and collaborate with product managers to turn ambiguous requirements into production software. The role requires strong backend or full-stack engineering, cloud infrastructure experience, and ownership of systems from prototype through production.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

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

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.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and cloud-based data platforms. Optimize systems for performance, reliability, security, and cost while implementing monitoring, governance, and data quality practices. Collaborate cross-functionally with data scientists, analysts, engineering, and product teams to deliver reliable data solutions and improve the data ecosystem roadmap.

Build and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and data platform tools. Develop cloud-based infrastructure using AWS and Terraform, optimize processing for reliability and cost, and implement monitoring, logging, governance, quality, and security practices. Collaborate with data scientists, analysts, autonomy engineers, and product teams to deliver reliable data solutions.

Build and maintain data warehouses, pipelines, scalable data models, and data quality frameworks for Waymo commercialization. Translate business requirements into database models, transform raw data for reporting and insights, ensure PII and ACL compliance, document solutions, and collaborate with data producers and consumers. The role requires SQL expertise, database knowledge, and programming experience in Python, C++, or Java, with preferred experience in distributed data processing and cross-functional project leadership.

Designs, deploys, and maintains hybrid data infrastructure across Vertica, SQL Server, PostgreSQL, Windows, Linux, virtualization, and shared storage environments. Develops Python and PowerShell automation, builds ETL pipelines, manages database performance and availability, administers hypervisors and operating systems, implements infrastructure as code, and leads incident response, monitoring, capacity planning, and post-mortems.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

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.

Prototype and integrate GPU-accelerated distributed data processing, compression, databases, and analytics solutions. Optimize complex data-intensive workloads across heterogeneous CPU/GPU architectures, collaborate on next-generation hardware and software designs, and work with customers and cloud service providers to deploy solutions and influence open standards. The role requires deep expertise in parallel programming, C/C++, accelerator architecture, memory systems, and compression or distributed data systems.

Lead the architecture, design, and implementation of a high-performance data processing engine focused on large-scale data processing.

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.

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.