Data Engineer Jobs in Santa Clara, CA

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 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.

Design and build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

The Principal GenAI Data Engineer will drive the design and implementation of scalable GenAI data platforms, focusing on robust data pipelines for structured and unstructured data. This role requires expertise in Python and AI data architecture, enhancing enterprise data capabilities.

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.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Design, build and own a petabyte-scale self-service real-time data enrichment platform. Lead query processing, ingestion pipelines, storage, indexing and performance optimization using Spark/Flink and Apache ecosystem tools. Deliver unified data catalog, improve query performance and platform efficiency, collaborate on technical reviews, and enable internal and external customers to query and extract data efficiently.

Lead development and delivery of healthcare AI/GenAI solutions for health systems—architect RAG and agentic workflows, manage global MLOps and model governance, engage executive stakeholders, and drive production-grade use cases across clinical, financial, and operational domains.

Lead development and implementation of advanced AI/ML solutions, design and optimize models and algorithms, transform raw data into actionable insights, manage projects end-to-end, supervise and mentor teams, maintain quality and standards, and manage client engagements and expectations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

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.

Develop and maintain data-protection features in CrowdStrike Falcon sensor on Windows, working across kernel and user mode. Implement filesystem minifilter drivers, AI-aware detection/protection logic, debug kernel issues, own features end-to-end, and collaborate with cross-functional teams to ship and support endpoint data security capabilities.

Build and own production-grade data systems that transform clinical, lab, and operational data into trusted, analysis-ready datasets. Collaborate with scientists, clinicians, and operations to design data models, services, and workflows enforcing integrity, access control, and regulatory compliance. Lead long-lived systems, improve data quality, monitoring, testing, and mentor engineers while supporting incident response and cross-functional adoption.

Lead design and implementation of multi-regional data platform and stream-processing architecture. Drive event-driven adoption, mentor engineers, ensure scalability, reliability, security, compliance, and participate in on-call operations.

As a Cloud Data Engineer, you'll build and maintain data pipelines, evaluate new technologies, and enhance AI outcomes. You'll collaborate with data scientists and develop applications in cloud environments using big data technologies.

The Data Center Optical Engineer ensures installation, maintenance, and operation of optical infrastructure in data centers, conducting audits and supervising optical testing.

Build and operate large-scale data systems for AI training and evaluation. Design ingestion, transformation, quality, lineage, and high-throughput delivery pipelines for ML workloads. Operate petabyte-scale storage, ensure reproducibility and dataset versioning, and collaborate across teams to optimize data infrastructure for model quality and training efficiency.

Design, deploy, and operate large-scale Apache Kafka/Confluent streaming platforms. Own cluster sizing, configuration, security, automation, observability, and developer enablement. Partner with cross-functional teams to translate requirements into reliable, performant solutions; perform code and design reviews, mentor junior engineers, and troubleshoot production issues.

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.

Lead AI-powered reporting and insights strategy, designing data architecture, pipelines, and LLM-enabled workflows (RAG, prompt engineering, agentic pipelines). Integrate BI tools and enterprise AI platforms, establish governance for executive AI outputs, and provide technical leadership to scale trusted, automated reporting and self-service insights across the organization.

Design, develop, and maintain scalable data pipelines and integration solutions. Build logical and physical data models, enforce data quality and governance, automate workflows (e.g., Airflow), troubleshoot systems, and collaborate with stakeholders to translate business requirements into reliable, cloud-based BI and analytics solutions.

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.

The Data Engineer will process raw data, develop predictive models, and conduct statistical analysis to support business decisions, collaborating with engineering teams and business partners.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.