Data Engineer Jobs in Irvine, CA

Design and develop AWS-based data platforms, build and maintain ETL pipelines using PySpark, perform data modeling and governance, integrate event-driven systems with Kafka, manage containerized deployments with Docker and Kubernetes, and produce clear documentation while collaborating with stakeholders.

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 maintain scalable data platforms, lakehouses, warehouses, ETL pipelines, Spark workflows, and AI/ML-integrated data solutions. Design dimensional models, implement governance and monitoring, optimize queries, integrate disparate data sources, and support KPI dashboards. Collaborate with engineering, product, DevOps, DBA, and security teams while mentoring engineers and analysts. Automate infrastructure and deployments using AWS, Terraform, and CI/CD practices.

Designs, implements, maintains, and secures Azure-based data platforms. Develops ETL/ELT pipelines, data warehouses, data lakes, data models, and cloud integrations. Administers Azure data services and databases, including performance tuning, backup, recovery, high availability, and disaster recovery. Automates operational tasks, supports CI/CD, monitors platform performance, resolves production incidents, and provides technical support to business and development teams.

Build, automate, monitor, and optimize batch and streaming data pipelines across cloud platforms and Snowflake. Responsibilities include troubleshooting failures, conducting root-cause analyses, orchestrating ETL/ELT workflows, managing connectors, implementing observability and data-quality controls, optimizing performance and costs, enforcing access controls, documenting procedures, and providing rotational on-call support. The role partners with product, analytics, platform, and architecture teams to deliver reliable enterprise data services.

Build and operate backend services, batch and real-time data pipelines, document-processing systems, APIs, integrations, and data models supporting an AI platform. Develop reliable OCR and LLM workloads, data quality monitoring, secure exports, cloud infrastructure, and CI/CD workflows. Design for distributed-system failures, authentication, authorization, and sensitive-data protection. Contribute to architecture decisions, maintain legacy services, support internal tools, mentor junior engineers, and improve engineering standards.

Architects, operates, and improves cloud data and analytics platforms and BI infrastructure. Owns platform standards, CI/CD, reliability, monitoring, incident response, security, governance, performance, and technical documentation. Leads onboarding and enables engineering and client teams through reusable patterns and guardrails. Requires production platform ownership, cloud data and BI expertise, accounting experience, scripting, SQL, troubleshooting, and cloud fundamentals.

Build, deploy, and own software applications, engineering-tool integrations, automations, data pipelines, and reporting systems. Manage AWS, Databricks, and GitLab CI/CD resources while ensuring reliability, security, and maintainability. Serve as product owner by gathering requirements, prioritizing roadmaps, communicating trade-offs, and supporting production tools. Collaborate with global engineering teams, process owners, and leadership.

Build and oversee scalable data platforms, warehouses, lakehouses, pipelines, ETL processes, and dimensional models using Databricks, Snowflake, AWS, Spark, Python, PySpark, and SQL. Integrate tracking data, optimize queries, implement governance and monitoring, and support AI/ML models. Partner with cross-functional teams, deliver dashboards and insights, mentor engineers and analysts, and automate infrastructure deployment using Terraform and CI/CD practices.

Build and operate scalable data-platform systems for multimodal robotics and machine-learning datasets. Responsibilities include ingestion, processing, validation, labeling, dataset generation, metadata, lineage, versioning, data quality, observability, and workflow reliability. Partner with ML, robotics, infrastructure, and labeling teams to create reusable platform capabilities supporting large-scale data processing, reprocessing, backfills, training, and evaluation.

Build and automate data-flywheel systems for autonomous robots, converting large-scale robot experiences into high-quality, versioned training datasets. Responsibilities include data processing, filtering, labeling, auto-labeling, offline inference, hard-example selection, validation, reproducible dataset generation, and incremental training-data pipelines. Partner with researchers and platform teams to productionize experimental workflows and improve the cycle from robot deployment to usable ML data.

Owns production data-platform components end to end, including ETL pipelines, data modeling, quality checks, monitoring, recovery, and cost optimization. Builds Spark on EMR and Airflow workflows, CDC ingestion, dimensional warehouse layers, and OLAP datasets. Partners with analysts and stakeholders to deliver reliable analytical data while tuning performance and automating reusable platform components.

Design, build, and maintain scalable data pipelines and architectures (ETL, data lake, MDM) using Snowflake and Talend Cloud. Ensure data quality and governance, enable AI-readiness, pilot AI/ML proofs-of-concept, support reporting (Tableau/Power BI), and manage data operations and external vendors.

Design, develop, and maintain databases and scalable data pipelines. Build stored procedures, triggers, and ETL processes; perform testing, tuning, security, and data quality work. Collaborate with developers and stakeholders, document architecture, review code, and support reporting and integrations to enable restaurant and customer-facing systems.

Design and optimize scalable geospatial data pipelines to convert high-frequency vehicle sensor telemetry into high-fidelity map features. Build map-matching, trajectory smoothing, and sensor-fusion algorithms, apply statistical tests for change detection and map validation, and lead system architecture for low-latency spatial indexing and storage while collaborating with embedded, perception, and cloud teams.

Early-career role to build, operate, and improve cloud data/analytics platforms (Fabric, Snowflake, Databricks). Support platform operations, CI/CD for analytics assets, onboarding for BI tools (Power BI/Tableau), runbooks, monitoring, incident response, and basic security/governance. Work under senior mentorship and contribute to automation, deployment templates, and performance triage.

Lead architecture, operation, and improvement of cloud data and analytics platforms (Fabric, Snowflake, Databricks). Own CI/CD for data assets, BI delivery (Power BI/Tableau), reliability, monitoring, incident response, security, and governance. Enable teams through standards, runbooks, and onboarding; troubleshoot performance and ensure production stability.

Design, build, and maintain scalable Python-based ETL/ELT data pipelines and Airflow workflows implementing Medallion (Bronze/Silver/Gold) Lakehouse patterns. Ensure data quality, performance tuning, monitoring, CI/CD, and cross-team collaboration to support analytics, reporting, and ML use cases while adhering to governance and security standards.

Design, build, and maintain data ingestion pipelines and integrations from intermittently-connected robots to analytics, ML training, and operations. Implement SDKs, connectors, schema contracts, retries, backfills, monitoring, and optimize pipeline performance, scalability, and cost. Collaborate with robotics, autonomy, and deployment teams to ensure reliable end-to-end data delivery and quality.

Build and operate the cloud foundation for the data platform: design compute, storage, and networking; manage IaC and GitOps; provision for large-scale sensor and ML workloads; own reliability, security, observability, cost management, and internal tooling to enable pipeline and analytics teams.

Design and build a scalable data platform and tooling for robotics/autonomy data ingestion, integration, and delivery. Build SDKs, connectors, and APIs (REST/gRPC, webhooks, CDC) to integrate diverse sources and downstream ML/BI systems, ensure schema/versioning/reliability, and optimize pipeline performance, scalability, and cost.

Design, build, and maintain production data pipelines and infrastructure for robot telemetry, logs, sensor data, and events. Implement ingestion, transformation, and orchestration (Airflow), backend services/APIs, and AWS-based storage/processing. Improve reliability with monitoring, retries, schema validation, and data quality checks. Collaborate with robotics, ML, and deployment teams to scale data systems for fleet operations.