Data Engineer Jobs in Sunnyvale, CA

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.

The Data Engineer will design, develop, and maintain scalable data systems, focusing on AWS and big data technologies to support data-driven decision-making and ensure data security.

Build and maintain large-scale data collection and preprocessing pipelines for NLP research. Develop web crawlers, APIs, and workflows; refine LLM outputs into structured datasets; collaborate with researchers to ensure data quality and document methodologies. Support scalable storage, retrieval, and distribution of datasets.

Build and maintain data pipelines using Python and Spark/PySpark. Work with Presto/Athena and Airflow to collect, aggregate, and store data powering BI. Emphasizes strong algorithms, data structures, and shipping high-quality software.

Build and scale optimized data pipelines and data engine architecture to run ML models, reduce annotation costs, integrate foundation models for labeling and QA, use software/hardware-in-the-loop testing, and collaborate with DoD customers to understand requirements and support field events.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

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.

The Staff Business Systems Engineer will manage systems supporting Data Center operations, focusing on capacity planning, lifecycle management, integrations, and asset tracking across infrastructure and finance. Responsibilities include architecture design, data modeling, and ensuring operational compliance.

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

The Data Engineer will design and manage data pipelines, develop datasets for product metrics, and collaborate with teams to ensure robust data integration and processing.

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.

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.

Design and build scalable pipelines and evaluation frameworks to compute driving-performance metrics across fleet and simulation data. Perform deep analysis of time-series, event, and geospatial data to identify regressions and root causes. Build geospatial analytics, release gating tooling, and LLM-powered agentic workflows to triage issues and propose fixes. Collaborate with engineering teams and adhere to QMS processes.

Design, build, and operate petabyte-scale data systems and pipelines to supply high-throughput, versioned, and lineage-tracked datasets to ML training and evaluation jobs. Ensure data quality, reproducibility, and efficient delivery for accelerator-based training while collaborating across teams and following strong software engineering practices.

Role focuses on ensuring data center security and resiliency, implementing controls and controls compliance, and protecting export-controlled information. The posting emphasizes eligibility to access export-controlled data and does not list specific technical duties or tools.

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Lead design, development, and deployment of data reporting and dashboard solutions for Client Service Training. Work with project leadership to create tables/files, run ad-hoc reports, validate results, automate scheduled reports or integrate with dashboard tools, and communicate insights using BI and statistical tools.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

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 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.

Build and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and data platform tools. Develop cloud-based infrastructure using AWS and Terraform, optimize processing for reliability and cost, and implement monitoring, logging, governance, quality, and security practices. Collaborate with data scientists, analysts, autonomy engineers, and product teams to deliver reliable data solutions.

Design and develop scalable data solutions and tooling to enable AI and advanced analytics across Azure hardware and software stacks. Identify opportunities for AI-driven impact, build data platforms and visualizations, and collaborate with engineers, data scientists, and program managers to improve efficiency, quality, and operational insights.

Build and consolidate data infrastructure for ML training: design automated ETL/ELT pipelines, implement DataOps practices (validation, monitoring, anomaly detection), integrate annotation workflows, manage dataset versioning and storage, and collaborate with ML engineers and operations to produce high-quality, reproducible training datasets.

Designs, deploys, and maintains hybrid data infrastructure across Vertica, SQL Server, PostgreSQL, Windows, Linux, virtualization, and shared storage environments. Develops Python and PowerShell automation, builds ETL pipelines, manages database performance and availability, administers hypervisors and operating systems, implements infrastructure as code, and leads incident response, monitoring, capacity planning, and post-mortems.