Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

The Senior Data Modeler is responsible for designing and managing data models, ensuring data accuracy, compliance, and alignment with business needs.

Build production-grade LLM applications (agentic chatbots, semantic search, RAG systems), fine-tune foundation models, implement RAG and agent architectures, integrate AI assistants with systems, design evaluation/governance pipelines, collaborate with cross-functional teams, and mentor junior data scientists.

Lead product and visual design for an optimization program: design internal tools and dashboards, create A/B test variations for email and digital channels, and produce high-quality communications (decks, newsletters, launch assets). Own end-to-end design from discovery through prototypes and developer handoff, maintain design system, and collaborate closely with data scientists, engineers, producers, and program stakeholders.

Lead supply chain analytics engagements: manage client teams, design data-driven solutions for demand forecasting, inventory and network optimization, drive key initiatives to closure, and provide thought leadership in supply chain analytics.

The Engagement Manager will lead analytics and AI-driven programs, manage client relationships, build high-performing teams, and innovate solutions using decision science and data science.

The Account Growth Manager will manage client relationships, drive strategic growth, and ensure successful sales and margin targets by leveraging AI and data-driven solutions.

Design, build, and optimize scalable Azure Databricks data pipelines and data lake/warehouse solutions. Develop ETL/ELT workflows using PySpark and Spark SQL, implement data ingestion, quality, monitoring, and CI/CD automation, and provide technical leadership while ensuring security, governance, and performance tuning for analytics and AI initiatives.

Design, build, and optimize scalable Azure Databricks data pipelines and data lakes. Develop ETL/ELT workflows using PySpark and Spark SQL, implement data quality and monitoring, optimize Spark jobs, support data warehouse initiatives, automate CI/CD deployments, and ensure security and governance while collaborating with stakeholders and mentoring junior engineers.

Design and govern scalable Azure data platforms; build ETL/ELT batch and streaming pipelines; implement lakehouse/warehouse and data quality frameworks; enable metadata, lineage, observability, CI/CD and IaC; lead migrations, mentor engineers, and work with stakeholders across BI, AI/ML, governance, and infrastructure.

Design, build, and optimize enterprise-scale data platforms and ETL/ELT pipelines on Azure Databricks. Implement Medallion/Lakehouse architectures, integrate streaming and batch sources, tune Spark jobs and clusters, create CI/CD for deployments, enforce governance/security, troubleshoot production issues, and mentor junior engineers.