Data Engineer Jobs in San Jose, 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.

Designs, builds, administers, and maintains scalable enterprise data platforms. The role ensures data reliability, security, governance, performance, compliance, and accessibility through monitoring, access management, incident response, automation, upgrades, migrations, and lifecycle management. It collaborates with data scientists, analysts, and product managers while optimizing cloud data infrastructure and supporting business intelligence, analytics, and future AI applications.

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.

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.

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 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.

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 maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Design, build, and operate cloud-native data platform components and services. Lead and deliver well-scoped projects, implement operators/controllers and automation, ensure reliability and observability, participate in on-call, and mentor junior engineers.

Leads distributed AI/ML, NLP, knowledge graph, and data intelligence teams. Sets technical strategy and oversees production systems for entity extraction, document intelligence, semantic modeling, GraphRAG, retrieval, and AI-ready data workflows. Drives model and data lifecycle management, evaluation, governance, reliability, automation, and responsible AI practices while partnering with engineering, product, security, quality, and customer-facing organizations. Builds talent, operating processes, and enterprise AI capabilities for regulated industries.