Top Data & Analytics Jobs in Santa Cruz, CA

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Design, build, and maintain data pipelines, dashboards, and predictive models. Analyze large datasets to generate actionable insights, collaborate with stakeholders, ensure data quality and governance, and document/share analytics best practices to support Marketing, Product, Operations, and Workforce teams.

Analyze large datasets, build SQL queries and data pipelines, develop predictive and statistical models, create dashboards, ensure data quality/governance, and translate insights into actionable recommendations for Marketing, Product, Operations, and Workforce teams.

Analyze large, complex datasets to generate actionable insights; build SQL queries and data pipelines; develop predictive models (regression, clustering, forecasting); create and maintain dashboards; ensure data quality and governance; collaborate with stakeholders to translate findings into business recommendations and document work.

Implement and maintain metadata management solutions (Collibra) across cloud data platforms. Build metadata ingestion, synchronization, lineage capture, and CDE frameworks. Integrate with Databricks Unity Catalog, support governance, stewardship, regulatory compliance, and produce technical documentation and governance artifacts.

Responsible for data governance and stewardship: maintain data quality and metadata, define and enforce data policies and standards, coordinate with business and technical stakeholders, and support compliance and data lifecycle management.

Develop and maintain ETL solutions, optimize processes, validate data, and support production issues. Collaborate with analysts for efficient data integration.

Analyze portfolio-level organic search performance to identify trends and opportunities, build dashboards and benchmarks, define KPIs, evaluate strategy and product impacts, partner with Product/Data/Engineering, and translate insights into clear recommendations for technical and non-technical stakeholders.

Lead development of Marriott's cloud data platform, build and own large-scale ETL pipelines on AWS and Snowflake, mentor engineers, manage service provider deliverables, ensure governance, data quality, deployment and production support, and research emerging AI/ML capabilities.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Owner of high-impact data products for corporate clients: design and run QA processes, investigate and resolve complex data quality issues, collaborate with Insights and Engineering, communicate with stakeholders, and identify opportunities to automate and improve workflows while mentoring peers.

Design, implement, and optimize enterprise data architecture and management for healthcare analytics. Lead data solutions using Microsoft Fabric and Power BI, ensure data governance, integrate imaging and EMR systems, troubleshoot ETL/SSIS, manage projects, mentor staff, and ensure HIPAA-compliant data practices.

Lead a team supporting Epic analytics and clinical/operational systems. Oversee daily operations, project coordination, testing, reporting, data governance, staffing, training, and vendor interactions. Design scalable data models and pipelines (Microsoft Fabric, SQL Server, Power BI), ensure data quality, and present findings to executive stakeholders while maintaining system availability and compliance.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.