Data Engineer Jobs in Sunnyvale, CA

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.

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.

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.

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.

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.

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.

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.

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, 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 analyze data quality, design prediction algorithms, collaborate with the engineering team, and generate business insights.

Design and implement foundational data systems for AI, focusing on efficiency and performance at scale. Collaborate on systems that optimize data handling and contribute to research advancements.

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 maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

New-graduate Data Scientist role building and deploying ML and GenAI systems at scale for cybersecurity. Responsibilities include model development, fine-tuning LLMs, embedding-based search, production-ready code, automated tests, architecture reviews, and cross-functional collaboration to deliver AI-powered security capabilities.

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.

Hybrid hardware/software engineer responsible for characterizing 4D LiDAR sensors, supporting customer integrations, building production Python/C++ data pipelines, running calibration and field debugging, maintaining ETL for high-volume sensor data, and coordinating data labeling and collection operations.

Design, deploy, and operate large-scale Apache Kafka/Confluent streaming platforms. Own cluster sizing, configuration, security, automation, HA/DR, observability, and developer enablement. Partner with cross-functional teams to translate requirements, conduct code/design reviews, mentor junior engineers, and ensure reliable, performant production streaming systems.

Design, build, and operate petabyte-scale data systems for ML training and evaluation. Responsibilities include data ingestion, transformation, lineage, versioning, quality assurance, and high-throughput delivery to accelerator-based training jobs, ensuring reproducibility and efficient model training while collaborating across engineering and cross-functional teams.

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.