Data Engineer Jobs in Sunnyvale, 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.

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 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 foundational dimensional data models, enterprise metrics, and curated data products on a modern lakehouse. Partner with business domains to translate processes into scalable, high-quality datasets, optimize analytical workloads, and operate distributed processing systems to support analytics, BI, and AI.

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 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.

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 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.

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.

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.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

Build and operate scalable streaming and batch data pipelines for monetization, financial, and operational data. Develop canonical data models, reusable data products, data quality controls, observability, lineage, reconciliation, and auditability. Partner with Product Engineering, Finance, Accounting, Analytics, and GTM teams to define data contracts and deliver reliable platform capabilities. Lead technical designs, complex cross-functional projects, incident response, and engineering improvements.

Build and maintain data warehouses, pipelines, scalable data models, and data quality frameworks for Waymo commercialization. Translate business requirements into database models, transform raw data for reporting and insights, ensure PII and ACL compliance, document solutions, and collaborate with data producers and consumers. The role requires SQL expertise, database knowledge, and programming experience in Python, C++, or Java, with preferred experience in distributed data processing and cross-functional project leadership.

Architects, deploys, secures, automates, and operates large-scale Apache Kafka and Confluent environments. Responsibilities include cluster sizing, high availability and disaster recovery, observability, infrastructure as code, troubleshooting, developer enablement, and collaboration with application teams. The engineer translates requirements into reliable production solutions, contributes to design and code reviews, documents systems, and mentors junior engineers.

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.

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, 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.

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.

Lead architecture and implementation of end-to-end data and analytics platforms for CoreAI. Design and optimize large-scale ETL/ELT pipelines, ensure data governance, quality, lineage, security, and enable self-service analytics. Collaborate with product, engineering, AI research, and business stakeholders to translate needs into roadmaps and data solutions. Mentor senior engineers and drive engineering standards, operational rigor, and scalable analytics for AI workloads.

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.