Top Analysis Reporting Jobs in Santa Cruz, CA

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.

Designs and implements scalable data lakehouse platforms on GCP, including BigQuery, GCS, BigLake, and Dataplex. Leads real-time and batch data pipeline development, data governance, metadata management, security, and performance optimization. Partners with clients and stakeholders to translate business needs into architecture, leads technical teams, mentors engineers, and provides hands-on solutioning and technical advisory support.

Supports rapid A/B testing across GiveDirectly programs by designing experiments, conducting power calculations, preparing high-quality datasets, analyzing results, and translating findings into actionable recommendations. The role coordinates with Programs, Product, Research teams, and external academic partners, manages multiple concurrent experiments, prepares learning products and de-identified datasets, and helps apply evidence across country programs. The position is remote from Rwanda with regular East Africa travel.

Own campaign measurement and reporting across video, podcasts, newsletters, social, digital, and live events. Build dashboards, benchmarks, partner-facing insight decks, case studies, and renewal materials. Monitor campaign KPIs, coordinate third-party measurement and brand lift studies, and turn audience and performance data into recommendations for pricing, packaging, content, and business growth.

Build and optimize Customer 360 data models, ELT pipelines, semantic layers, and identity resolution frameworks in BigQuery. Develop Tableau dashboards for customer acquisition, retention, churn, product adoption, and deposits. Sync behavioral data with marketing and CRM platforms such as Braze, implement data quality and observability controls, and partner with Marketing, Product, and business leaders to turn customer analytics into growth initiatives.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Leads consumer, category, market, and growth insights across infant formula and new ventures. Supports qualitative and quantitative research, opportunity sizing, commercialization, pricing, go-to-market strategy, KPI dashboards, syndicated data analysis, campaign evaluation, launch measurement, and executive reporting. Partners cross-functionally with Innovation, Marketing, Sales, Product Development, Finance, research agencies, and data providers to translate intelligence into actionable growth recommendations.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Build and scale secure data lakehouse and warehouse infrastructure supporting analytics, machine learning, clinical AI performance monitoring, and self-service insights. Develop scalable data pipelines, models, orchestration, quality controls, and LLM-enabled analytics tools using heterogeneous clinical and EHR data. Collaborate with engineering, data science, clinical, product, and client success teams to establish trustworthy metrics and technical solutions.