Data Engineer Jobs in San Jose, CA

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Architect and operate RaptorDB database platforms across data centers and public clouds. Lead deployment, backup, restore, replication, failover, resilience, performance optimization, and cloud-native adoption. Develop maintainable software, automation, testing, and deployment pipelines while collaborating across engineering, infrastructure, security, and support teams. Provide technical leadership during production incidents, establish architecture standards, and document operational knowledge.

Develop scalable Java software for platform persistence, data lifecycle management, concurrency, distributed storage, and relational systems. Improve data performance, security, loss detection, and recovery while collaborating across teams. Deploy and manage services on Kubernetes, work with S3 and Apache Iceberg, support telemetry and visualization initiatives, and apply AI-assisted development practices with appropriate guardrails.

Act as Micron's technical advisor for datacenter customers to drive DRAM/HBM/SSD design wins, resolve system configuration and performance issues, guide memory architecture and optimization, support validation/lab testing and deployments, and collaborate across sales, product, and engineering teams.

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.

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.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads architecture and development of ServiceNow’s enterprise data access layer across multiple database engines. Owns technical direction, data architecture, feature delivery, code and design reviews, testing strategy, performance optimization, production troubleshooting, and cross-team solutions. The role requires deep Java and backend platform expertise, distributed systems knowledge, database experience, technical leadership, mentoring, and responsibility for scalable, reliable, secure platform capabilities.

Lead architecture, roadmap, and implementation for Veza’s scalable Access Graph data platform. Solve complex distributed-systems and graph-database challenges, establish engineering standards, drive high-impact technical initiatives, and ensure correctness, performance, testing, and rollback safety. Collaborate across product and engineering teams, mentor engineers, represent technical roadmaps, and use AI-assisted development tools with rigorous verification.

Leads the design, development, and operation of highly available, multi-tenant data platform services on Kubernetes, including Postgres, streaming systems, and key-value stores. Defines distributed-systems architecture, failover, disaster recovery, observability, scalability, and SLOs. Builds operators, controllers, automation, and self-service APIs; drives major technical initiatives; partners across engineering; leads reliability efforts; and mentors engineers. The role requires deep hands-on software development and operation of stateful systems at scale.

Build and operate cloud-native data platform components and services from design through production. Lead scoped projects, create technical designs, improve reliability and scalability, write tests and instrumentation, maintain runbooks, and participate in on-call rotations. Develop Kubernetes operators, controllers, automation, and platform services. Collaborate with senior engineers and mentor junior team members through code reviews, design feedback, and pairing.

Develop and manage Workday PSA and Financials data solutions, including conversions, transformations, integrations, reporting, APIs, EIBs, and ETL processes. Create technical documentation, perform data analysis and loading, support migrations and M&A initiatives, and ensure security, compliance, access controls, and data quality. Partner with business analysts, functional leads, engineers, and security teams while managing multiple projects and serving as a Workday data subject matter expert.

Build and ship high-impact full-stack features using React, NodeJS, MongoDB, AWS, and Snowflake. Own vertically sliced functionality across the full software development lifecycle, including design, coding, testing, code review, deployment, API development, and production support. Collaborate with engineering, sales, stakeholders, and customers to prioritize product opportunities, incorporate feedback, and improve engineering processes and systems.

Build and manage complex ETL and data pipelines, data warehouses, monitoring, and alerting systems for a legal-focused infrastructure team. Lead cross-functional data engineering initiatives from planning through delivery, optimize large-scale data processing, identify reliability issues, and communicate with technical and non-technical stakeholders. The role also involves supporting consulting practice activities, documentation, thought leadership, and client-facing deliverables.

Build reusable Python libraries and reproducible analysis pipelines for image and experimental data; define data models and provenance; partner with wet‑lab scientists to translate requirements and validate methods; establish testing, validation, versioning, and observability for scientific workflows.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

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 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.

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).