Data Engineer Jobs in Santa Clara, CA

The role involves designing and developing data applications, working with large data sets, and requires expertise with various data processing technologies. Candidates should have strong analytical skills and experience in prototyping software applications.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

The Software Engineer will develop and manage data platforms for CoreWeave, focusing on performance, security, and scalability while facilitating data access and compliance with regulations.

Design and implement data platforms, manage data infrastructure, develop streaming architectures, improve performance, and ensure data compliance.

Design, build, and operate cloud-native data platform components and services. Lead and deliver well-scoped projects, implement operators/controllers and automation, ensure reliability and observability, participate in on-call, and mentor junior engineers.

Lead architecture and standards for CoreWeave's enterprise data ecosystem: design lakehouse/streamhouse architectures, modeling and semantic layers, governance, data quality, lineage, observability, and reusable data products. Drive cross-domain solutions, resolve performance and scalability constraints, evaluate core data technologies, conduct architecture reviews, and mentor senior engineers.

Leads distributed AI/ML, NLP, knowledge graph, and data intelligence teams. Sets technical strategy and oversees production systems for entity extraction, document intelligence, semantic modeling, GraphRAG, retrieval, and AI-ready data workflows. Drives model and data lifecycle management, evaluation, governance, reliability, automation, and responsible AI practices while partnering with engineering, product, security, quality, and customer-facing organizations. Builds talent, operating processes, and enterprise AI capabilities for regulated industries.

Leads mechanical engineering for medium- to large-scale data center projects, overseeing multidisciplinary teams, technical designs, quality, budgets, schedules, client coordination, contractor discussions, field verification, construction issue resolution, and staff development. Produces plans, specifications, drawings, and models for HVAC, cooling, piping, plumbing, fire protection, and related systems while supporting proposals and project travel.

Build and own an internal manufacturing data platform, including a web portal, REST API, databases, automated pipelines, dashboards, data validation, traceability, cost and inventory tools, and reporting. Collaborate with engineering, test, operations, and supply-chain teams across sites; administer access, security, backups, and documentation; train users and drive adoption.

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 Data Engineer will design, develop, and maintain scalable data systems, focusing on AWS and big data technologies to support data-driven decision-making and ensure data security.

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.

Build and maintain large-scale data collection and preprocessing pipelines for NLP research. Develop web crawlers, APIs, and workflows; refine LLM outputs into structured datasets; collaborate with researchers to ensure data quality and document methodologies. Support scalable storage, retrieval, and distribution of datasets.

Build and maintain data pipelines using Python and Spark/PySpark. Work with Presto/Athena and Airflow to collect, aggregate, and store data powering BI. Emphasizes strong algorithms, data structures, and shipping high-quality software.

Build and scale optimized data pipelines and data engine architecture to run ML models, reduce annotation costs, integrate foundation models for labeling and QA, use software/hardware-in-the-loop testing, and collaborate with DoD customers to understand requirements and support field events.

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.

The Staff Business Systems Engineer will manage systems supporting Data Center operations, focusing on capacity planning, lifecycle management, integrations, and asset tracking across infrastructure and finance. Responsibilities include architecture design, data modeling, and ensuring operational compliance.

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.