Data Engineer Jobs in San Jose, CA

Build and lead scalable data platforms, ETL frameworks, streaming pipelines, data lakes, warehouses, and transformation tools. Develop Python- and Spark-based systems for extracting, validating, transforming, and analyzing semiconductor testing data. Support self-service machine learning and analytics platforms, real-time processing, custom visualizations, and complex data ecosystems. Collaborate with IT and business teams to define data strategies, gather requirements, plan projects, and deliver production-ready solutions.

Design, build, and operate high-throughput, fault-tolerant data ingestion and transformation pipelines and a lakehouse for large-scale ML training and inference. Ensure data quality, lineage, and versioning; partner with ML engineers on feature stores, dataset contracts, and performance optimization.

The CRM System Administrator and Data Engineer will manage Dynamics 365 and the data warehouse, collaborating with users to optimize processes and support system integration.

Act as Micron's technical advisor for datacenter customers to drive DRAM/HBM/SSD design wins, resolve system configuration and performance issues, guide memory architecture and optimization, support validation/lab testing and deployments, and collaborate across sales, product, and engineering teams.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Build and lead NVIDIA’s enterprise data governance platform, covering sensitive-information detection, governed access, export-control enforcement, audit logging, data connectors, and AI knowledge readiness. Design scalable backend, distributed, and data-processing systems; integrate enterprise content sources; evaluate ML classification models; enforce access-control fidelity; and ensure ingestion quality. Provide technical leadership through architecture decisions, code and design reviews, mentorship, and cross-functional alignment with Security, Legal, Finance, and platform teams.

Lead design and evolution of FloQast's core data platform and lakehouse, owning event streaming, batch and streaming ETL, storage, governance, and compute architecture (Spark). Define standards, mentor engineers, collaborate cross-functionally, and ensure scalable, secure, production-grade data infrastructure for product and analytics.

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

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

Design and build scalable pipelines and evaluation frameworks to compute driving-performance metrics across fleet and simulation data. Perform deep analysis of time-series, event, and geospatial data to identify regressions and root causes. Build geospatial analytics, release gating tooling, and LLM-powered agentic workflows to triage issues and propose fixes. Collaborate with engineering teams and adhere to QMS processes.

Build and own data pipelines, models, quality checks, dashboards, and visualizations for manufacturing and test data. Partner with optical, manufacturing, and quality engineers to create reliable data solutions, improve pipeline performance and cost, and support engineering and leadership decisions. Establish software engineering practices for data systems, guide technical direction across distributed sites, and mentor other engineers.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.

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.

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

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 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.

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.