Data Engineer Jobs in Sunnyvale, CA

Lead the design and optimization of enterprise-scale data warehousing and analytics solutions, utilizing Snowflake and advanced SQL for global business operations and decision-making.

The role involves developing large-scale data processing pipelines, automating data selection, and collaborating on ML model improvements to enhance vehicle performance and safety.

Build and maintain high-throughput C++ recording systems and Python data services for vehicle sensor and telemetry data. Work on data transfer, ingestion, validation, format/versioning, indexing, compression, and efficient access for replay, analytics, and ML while operating under vehicle resource constraints and contributing to quality management.

Design, build, and operate reliable, scalable backend services and platform capabilities for monetization (pricing, billing, ads, payments). Own architecture, integrations, correctness, observability, and operational excellence. Lead cross-functional technical projects, write RFCs, mentor engineers, and deliver reusable developer tools and data products for product, finance, and GTM teams.

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 role involves bridging machine learning and systems engineering to enhance autonomy performance by optimizing data quality and developing frameworks for synthetic data in autonomous systems.

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.

Build and optimize edge and cloud data pipelines for perception ML models. Scale data engine for high-fidelity labels, reduce annotation costs, integrate foundation models for automated labeling and QA, leverage software/hardware-in-the-loop testing, and support DoD field use cases and model deployment lifecycle.

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.

You will create large datasets and training recipes, develop methods for data mining, create scalable infrastructure solutions, and collaborate with ML infrastructure teams.

Own and develop concurrent C++ backend services for Webviz to stream time-series and sensor data, integrate offboard storage and WebRTC, optimize latency and throughput, build APIs for automated triage/evaluation, plan technical roadmaps, and mentor engineers.

Build next generation distributed data storage and processing systems, including projects like Apache Spark and Delta Lake, while focusing on customer value and handling complex data engineering pipelines.

As a Senior Software Engineer at Databricks, you will design and build advanced distributed data storage and processing systems focusing on optimizing performance and supporting various data workloads.

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.

Build and productionize large-scale data pipelines and platforms to generate ML and evaluation datasets from driving logs and simulation data. Design tools for data mining, exploration, and analysis, and collaborate with Data Science and Quantitative Analytics to deliver high-impact, production-ready infrastructure and dataset generation.

The Big Data Developer (Spark) will be responsible for Spark and Scala development, Hadoop performance tuning, and SQL coding. Requires extensive experience in data processing and performance optimization.

Lead architecture and implementation of scalable ML data pipelines and tooling to turn autonomy data into training datasets. Build data selection, active learning, inference-at-scale, vector DB management, labeling integration, QC automation, and introspection tools while partnering with ML researchers and infra teams.

As a Principal Data Processing Engineer, you will enhance a data processing engine, collaborate on open-source platforms, and optimize performance.

As a Senior Data Engineer at TRI, you will design and build robust data infrastructure and pipelines to support autonomy research and simulation workflows.

Design, build, and maintain scalable data lake and warehouse infrastructure for large-scale autonomous driving data. Identify and resolve edge cases by analyzing field data, collaborate with ML and annotation teams, monitor metrics, manage data quality, schemas, access controls, and optimize ETL/ELT and processing pipelines to improve system performance and edge-case resolution workflows.