Data Engineer Jobs in Santa Clara, CA

Build and operate production Kubernetes-based compute platform for large-scale data and ML workloads. Improve cluster reliability, GitOps delivery, multi-tenant scheduling, resource isolation, GPU workload orchestration, and reusable batch/workflow systems for petabyte-scale processing while contributing to QMS and continuous improvement.

Design, build, and maintain scalable Databricks (PySpark/SQL/Delta Lake) pipelines to transform SAP S/4HANA and ECC data into curated, governed data products. Implement data quality, lineage, observability, and access controls (Unity Catalog, Immuta), collaborate with SAP experts and analytics teams, and mentor engineers to deliver production‑grade datasets for analytics and AI/ML.

The Senior Staff Physical AI Data Algorithm Engineer is responsible for designing and optimizing a vehicle-cloud data closed-loop architecture, managing data toolchains, and ensuring high-quality data standards throughout the model development cycle.

The Design Data Integrity Engineer ensures the accuracy and compliance of engineering drawings and models, providing quality checks and support for manufacturability.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

As a Data Science and Machine Learning Software Engineer at NetApp, you will develop algorithms for real-time personalized search and recommendation using IoT data, while working on cloud-based services and analytics.

As a Software Engineer, you will enhance the data infrastructure by building new concurrency controls, indexing schemes, and optimizing distributed data processing algorithms.

Design, develop, and test datacenter/SDN networking solutions focused on Layer 3 protocols. Produce architecture and design artifacts, support implementation and transition, automate testing, and resolve SDLC issues while coordinating with stakeholders.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Develop and enhance a retail engagement analytics product by implementing data engineering solutions: build real-time data flows, work with NoSQL stores, support data infrastructure and visualization, clean and prep noisy datasets, and recommend improvements to data reliability, efficiency, and quality.