Data Engineer Jobs in Santa Clara, CA

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.

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.

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.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.

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.

Design, build, and maintain data transfers and databases. Produce efficient, reusable data engineering code, perform data modeling, UAT, data cleaning, reporting, and technical documentation. Collaborate with stakeholders, participate in code reviews, and improve automation and processes to meet business requirements.

Lead diverse technology projects and a team to create solutions for regulatory needs, leveraging distributed microservices and collaboration with product managers.

Build and maintain production-grade ETL/ELT pipelines for large supply-chain datasets (wafers, memory, assemblies). Ensure data quality, curate datasets, design optimized schemas, and enable downstream ML/AI models. Collaborate with procurement, operations, engineering, and IT to centralize legacy workflows and feed unified forecasting systems.

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, operate, and maintain flight test data systems for structural loads measurement; develop and validate loads equations, process large flight datasets, build automated pipelines, produce fatigue and loads spectra, compare test data to models, support stress and damage tolerance analysis, and compile certification deliverables while guiding instrumentation strategy.

Lead MLOps engineering for edge and cloud pipelines that ingest and label video for perception models. Build optimized data pipelines, scale labeling and annotation, integrate foundation models for automation, apply software- and hardware-in-the-loop testing, and collaborate with DoD stakeholders to meet field requirements.

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.

Lead development of AI and ML solutions for healthcare systems, overseeing teams, driving use case development, and ensuring operational excellence while managing global delivery operations.

As a Cloud Data Engineer, you'll build and maintain data pipelines, evaluate new technologies, and enhance AI outcomes. You'll collaborate with data scientists and develop applications in cloud environments using big data technologies.

The Data Engineer will process raw data, develop predictive models, and conduct statistical analysis to support business decisions, collaborating with engineering teams and business partners.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

The Data Engineer will gather and process data, analyze trends, support business decisions, and collaborate on predictive models while improving algorithms and efficiency.

The role involves designing and developing data applications, working with large data sets, and requires expertise with various data processing technologies. Candidates should have strong analytical skills and experience in prototyping software applications.

Design and implement cloud-native data management services (catalog, metadata, dataset/checkpoint lifecycle) for exabyte-scale GPU workloads. Build backend systems on Kubernetes and major cloud providers, collaborate with research and cross-functional teams, document architecture, and drive integration with storage/compute innovations (GPU Direct Storage, DPU).

Design, build, and operate large-scale Hadoop-based data pipelines and streaming systems. Implement ETL/ELT with Spark, Hive, Pig, and Sqoop; build Kafka/Flink streaming; optimize Spark/MapReduce jobs; design storage layouts (HDFS, Hive, HBase, Parquet/ORC/Delta/Iceberg/Hudi); enforce data governance, monitoring, and orchestration (Airflow/Oozie); collaborate with data scientists; produce documentation and runbooks; mentor junior engineers.

Design, build, and own scalable batch and streaming data pipelines and data models for analytics and ML. Collaborate with data science, engineering, and product to deliver data products and dashboards, enforce data quality and testing, mentor engineers, and set technical direction for the data platform.

Lead architecture and development of data-intensive services and APIs for robotic platforms. Design ingestion and processing pipelines, define interfaces and data contracts, implement comprehensive testing and CI/CD, drive observability and operational excellence, collaborate cross-functionally, ensure security and compliance, and mentor engineering teams.

Lead design, build, and maintenance of scalable data pipelines, sandbox databases, and analytics dashboards. Investigate and resolve complex data discrepancies, perform ETL/ELT, data modeling, and performance tuning using Databricks, Starburst, Kafka, SQL, and Python. Partner cross-functionally, enforce data governance, document processes, and mentor team members to support research prototyping and business insights.