Data Engineer Jobs in Santa Clara, CA

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.

Lead AI-powered reporting and insights strategy, design reporting data architecture and pipelines, and implement LLM-powered workflows (RAG, prompt engineering, agentic pipelines). Integrate BI tools and enterprise AI platforms, establish governance for AI outputs, and provide technical leadership to deliver trusted executive insights and scalable self-service reporting.

Build and operate a security data platform connecting SIEM, identity, asset, threat intelligence, and knowledge graph systems. Design core APIs, distributed data pipelines, schemas, risk scoring, entity resolution, and coverage queries. Own architecture and platform workstreams from scoping through delivery, collaborate with detection and identity teams, review code, mentor engineers, and drive partner adoption. The role requires expert Python, deep Splunk expertise, cybersecurity experience, and senior-level project ownership.

Design, build, and maintain large-scale GCP data pipelines and data warehouse solutions using Dataproc, GCS, BigQuery, Hadoop/Spark/Hive and Airflow. Mentor engineers, run agile ceremonies, prioritize backlog, provide on-call support, and deliver data models and products at multi-TB/PB scale.

Designs and operates billion-scale distributed data platforms across streaming, near-real-time, micro-batch, and batch workloads. Owns advertising and marketing data domains, building reliable, scalable, tenant-aware pipelines with strong latency, quality, observability, privacy, and cost controls. Optimizes Spark, Kafka, BigQuery, Dataproc, storage, and orchestration systems. Leads architecture reviews, mentors engineers, improves incident response, and enables governed agentic workflows across global advertising products.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Design and maintain automated testing across data pipelines, ETL/ELT workflows, REST APIs, backend services, and cloud data warehouses. Validate data integrity, schemas, transformations, reconciliations, performance, and reliability for ERP data flows. Integrate tests into CI/CD, manage synthetic test data, investigate discrepancies, support releases, and mentor junior quality engineers while promoting data-quality-focused testing practices.

Lead architecture, roadmap, and implementation for Veza’s scalable Access Graph data platform. Solve complex distributed-systems and graph-database challenges, establish engineering standards, improve reliability and performance, and deliver backend services in Go. Drive cross-team planning, technical decisions, design reviews, and stakeholder alignment while mentoring engineers and team leads. Ensure high standards for correctness, testing, documentation, support, rollback safety, and responsible use of AI-assisted engineering tools.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

Architect and build large-scale distributed data platform components, ingestion pipelines, and Data Lake solutions. Lead complex technical initiatives, develop reusable libraries and frameworks, optimize Java and JVM performance, and ensure reliable, scalable operations. Collaborate across teams, establish engineering best practices, evaluate emerging technologies, and troubleshoot production issues across distributed systems using Kafka, Apache Iceberg, Apache Spark, and related data technologies.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Develop machine learning and hybrid physics-data methods to improve vehicle and component models. Build scalable pipelines for vehicle telemetry, simulation, test, and fleet data, including ingestion, synchronization, transformation, storage, and evaluation. Create cloud infrastructure for model training, simulation, validation, experiment tracking, and reproducible development supporting vehicle intelligence and autonomous driving.

Leads architecture and development for ServiceNow’s enterprise data access platform. Responsibilities include improving scalable, secure, high-performance data access; developing SQL generation and database metadata capabilities; troubleshooting complex production issues; driving architecture, design, code reviews, testing, and reliability practices; delivering cross-team projects; and mentoring engineers across levels. The role requires expert Java and backend platform development, relational database expertise, distributed systems knowledge, and technical leadership across teams.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

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.

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.

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.

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.

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.

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.