Data Engineer Jobs in San Jose, CA

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

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.

Design, build, and scale the Veza Access Graph backend: develop graph features, improve performance, reliability, and APIs, collaborate cross-functionally, and implement monitoring, logging, and compliance capabilities for identity and access management at scale.

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 AI-powered reporting and insights strategy, design reporting data architecture and pipelines, and implement LLM-powered workflows (RAG, prompt engineering, agentic pipelines). Integrate BI tools and enterprise AI platforms, establish governance for AI outputs, and provide technical leadership to deliver trusted executive insights and scalable self-service reporting.

Design, build, and operate large-scale security data pipelines, lake/lakehouse storage, and analytics to normalize telemetry from a massive GPU fleet. Ensure data platform security (encryption, RBAC/ABAC, audit), enable trustworthy analytics and detection, collaborate across teams to define data contracts, and maintain data quality, provenance, and freshness for security posture and automated response.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.

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.

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.

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.

Lead design and operation of Block's real-time, event-driven data systems. Build and run high-throughput streaming pipelines, turn Kafka event streams into low-latency data products, improve reliability and observability, mentor engineers, and define platform architecture and standards for projections, third-party data proxy, and graph data capabilities.

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.

Design, build, and operate petabyte-scale data systems and pipelines to supply high-throughput, versioned, and lineage-tracked datasets to ML training and evaluation jobs. Ensure data quality, reproducibility, and efficient delivery for accelerator-based training while collaborating across teams and following strong software engineering practices.

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.

Design, build, and maintain data transfers and databases. Produce efficient, reusable data engineering code, perform data modeling, UAT, data cleaning, reporting, and technical documentation. Collaborate with stakeholders, participate in code reviews, and improve automation and processes to meet business requirements.

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