Data Engineer Jobs in Santa Ana, CA

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.

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.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

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.

Design, develop, and maintain SSRS reports and Power BI dashboards; perform data modeling and DAX calculations; support ETL (SSIS) and validate data across EMR/billing/CRM systems; gather and translate stakeholder requirements; liaise with offshore teams; perform testing, documentation, and ensure HIPAA/PHI compliance and data governance.

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 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.