Data Engineer Jobs in Fremont, 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.

Analyzes wafer fabrication, equipment, sensor, metrology, and manufacturing data using AI, machine learning, statistics, and data-mining techniques. Develops data pipelines, analytical tools, dashboards, deployed models, and monitoring methods for anomaly detection, yield prediction, excursion analysis, and root-cause investigation. Collaborates with engineering, manufacturing, MES, and AI/ML teams to translate results into actionable insights, support experiments, improve processes, and establish responsible AI and data-quality practices.

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.

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.

Builds and maintains scalable data pipelines, ETL infrastructure, data warehouses, and analytics tools. The role assembles complex datasets, automates processes, optimizes data delivery architecture, and supports cross-functional data initiatives. Collaborates with executive, product, data, and design stakeholders to resolve technical issues and improve analytics systems. Requires proficiency in SAS, Python, Java, MATLAB, SQL, NoSQL, Hadoop, MapReduce, Hive, Presto, UNIX, and Linux, with JavaScript, R, and basic machine learning preferred.

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.

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.

Develops and manages data pipelines with Snowflake, SQL, dbt, and Airflow; builds REST APIs and microservices using Python and FastAPI; supports Salesforce Data Cloud; implements CI/CD; creates reusable Slack app frameworks; and collaborates cross-functionally on scalable solutions.

Supports product launches and lifecycle management by maintaining accurate master data across PLM, ERP, and global item management systems. The role monitors data integrity, resolves discrepancies, supports product changes and obsolescence, follows regulated data governance procedures, analyzes trends, and recommends process improvements. It also contributes to documentation, training, and scalable data management practices while collaborating with project management, operations, supply chain, quality, and commercial teams.

Manage product master and transactional data across PLM, ERP, and global item management systems. Support product launches, lifecycle changes, and discontinuations while maintaining data accuracy, integrity, quality compliance, and regulatory alignment. Analyze trends, resolve discrepancies, improve processes, document procedures, develop training materials, and collaborate with project managers and cross-functional stakeholders. The role requires business systems and database experience, advanced Excel proficiency, and familiarity with enterprise systems, regulated industries, and data-focused platforms.

Supports product launches and lifecycle management by maintaining accurate master data across PLM, ERP, and global item management systems. The role monitors data integrity, resolves discrepancies, supports data governance and regulatory procedures, analyzes trends, and recommends process improvements. It partners with project management, operations, supply chain, quality, and commercial teams, while contributing to documentation, training, and scalable data management practices in a regulated medical device environment.

Manage product master and transactional data across PLM, ERP, and global item management systems. Support product launches, lifecycle changes, and discontinuations while ensuring data accuracy, integrity, regulatory compliance, and operational readiness. Monitor and resolve discrepancies, analyze trends, improve data processes, maintain documentation and training materials, and collaborate with project managers and cross-functional stakeholders on system setup and change requests.

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

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

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.

Design and maintain scalable data pipelines, models, curated datasets, analytics solutions, and manufacturing automation tools. Support Ignition and Sepasoft MES systems, integrate manufacturing software, and develop solutions for metrology and image inspection data. Apply software engineering practices and independently lead complex cross-functional projects from requirements through deployment.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

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.

As a Business Intelligence Engineer, you will architect data insights, challenge existing metrics, perform deep-dive analyses, design innovative models, and collaborate across teams to drive data solutions.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.