Data Engineer Jobs in Santa Clara, CA

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Lead design, development, and deployment of data reporting and dashboard solutions for Client Service Training. Work with project leadership to create tables/files, run ad-hoc reports, validate results, automate scheduled reports or integrate with dashboard tools, and communicate insights using BI and statistical tools.

Build and scale the platform powering Wayve’s data flywheel and foundation-model stack. Responsibilities include developing large-scale data curation, enrichment, evaluation, training, and serving infrastructure; implementing distributed processing, vector search, lakehouse, dataset versioning, and annotation systems; and transforming manual workflows into reliable self-service products. The role requires production ownership, observability, testing, on-call support, and close collaboration with Applied Scientists and ML Engineers.

Lead diverse technology projects and a team to create solutions for regulatory needs, leveraging distributed microservices and collaboration with product managers.

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 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.

Design, operate, and maintain flight test data systems for structural loads measurement; develop and validate loads equations, process large flight datasets, build automated pipelines, produce fatigue and loads spectra, compare test data to models, support stress and damage tolerance analysis, and compile certification deliverables while guiding instrumentation strategy.

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.

The Principal Software Engineer will develop backend systems using Rust or Go, debug integrations, manage middleware components, and optimize data flows while following SDLC practices and collaborating with teams.

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.

Analyze critical stack-component data for yield, performance, and capacity improvements. Develop engineering software tools and image-processing algorithms for characterization and metrology, collaborate cross-functionally on yield and quality improvements, and evaluate software and hardware for new component characterization metrics.

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.

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.

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.

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 and optimize high-performance C++/CUDA libraries for GPU-based data processing. Improve multi-node GPU communication (UcxExchange), optimize Presto GPU query performance, and lead enhancements across Presto, Velox, and cuDF projects for efficient DataFrame and database acceleration.