Top Data Engineer Jobs in Santa Cruz, CA

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

The Sr. Data Engineer will build and maintain data pipelines, develop ETL processes, and optimize data infrastructure, collaborating across teams to ensure data reliability and performance.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

Lead architecture and implementation of scalable data systems and pipelines from ingestion to delivery. Own ambiguous, high-impact integrations, improve data quality and reliability, adopt AI-augmented engineering, mentor engineers, and collaborate with Product, ML, and customer teams to ship production solutions.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.

Design, build, and operate production-grade data ingestion pipelines and dbt transformations into a Databricks lakehouse. Establish data quality, monitoring, and observability. Partner with Product and Engineering to enable metrics, dashboards, and longitudinal patient analytics supporting clinical and operational decision-making.

The Staff Data Engineer will enhance the data ecosystem at WeightWatchers by developing data pipelines, collaborating cross-functionally, and ensuring data quality while leading projects and technical initiatives.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

Design, implement, and support enterprise data integrations connecting JDE, Workday, Salesforce and other platforms. Lead SQL-driven ETL/ELT pipelines, develop reusable integration frameworks, ensure data integrity and audit readiness, perform regression and production troubleshooting, and provide architectural oversight and mentorship across multiple concurrent projects.

Design and implement data infrastructure and pipelines, ensuring reliability and quality. Collaborate with teams for data solutions and optimize processes. Coach junior engineers.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Design, build, and maintain data models, pipelines, and access layers to support care delivery, operations, and analytics. Ensure data quality, governance, and reliability while optimizing infrastructure and performance. Serve as a technical leader, review code, set engineering best practices, and collaborate cross-functionally to translate business needs into scalable data solutions.

Lead architecture and implementation of cloud-native data platforms on Google Cloud (BigQuery). Build unified batch/streaming pipelines, enforce IaC with Terraform, implement open table formats and governance (Dataplex/Data Catalog), automate data quality/lineage, partner with analytics and product teams, and mentor data engineers.

Design and automate data validation and testing across ETL/ELT pipelines during migration to Microsoft Fabric. Build automated data quality frameworks, embed tests into CI/CD, validate Medallion layers, perform performance/load testing, and configure alerting and incident triage to ensure data integrity for analytics consumers.

The Data Architect/Data Governance Lead supports the Medicaid Enterprise Data Warehouse by leading data architecture, metadata management, and governance processes, ensuring compliance and scalability.

As a Sr. Data Engineer at Lyra Health, you will build and scale core data infrastructure, design data warehouses, and create efficient data pipelines while collaborating across departments.

The Senior Data Engineer leads the design and optimization of scalable data pipelines for AI-driven analytics while collaborating with cross-functional teams. Responsibilities include ensuring data integrity, automation, and compliance in cloud environments, particularly on Google Cloud Platform, to enhance decision-making processes.

In this role, you will design data architecture, lead integration strategies, mentor engineers, and drive AI infrastructure for cross-division data systems.

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

The Data Ingestion Specialist will manage the ingestion of healthcare data, improve data models, debug issues, and collaborate with team members to maintain pipeline reliability and efficiency.

As a Senior Data Engineer, you'll transform raw data into datasets, design production pipelines, integrate customer data, and collaborate with various teams to ensure data quality and reliability.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead design and delivery of large-scale data pipelines and warehouse systems. Define technical strategy, enforce data governance, improve developer efficiency, mentor engineers, and align data engineering efforts with business goals to enable analytics and ML at scale.