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

Owns the vision, roadmap, prioritization, delivery, adoption, and performance of enterprise membership data products. Partners with membership, marketing, CRM, engineering, analytics, security, privacy, and governance teams to define identity, lifecycle, quality, and KPI requirements. Oversees governed Databricks data products supporting activation, experimentation, personalization, reporting, and AI use cases while ensuring privacy and responsible use. Requires strong product leadership, data product expertise, stakeholder influence, and knowledge of membership or loyalty strategies.

Develops and maintains full-stack software applications. The posting provides compensation, benefits, contractor availability, and application timing information but does not specify particular technical responsibilities, technologies, or qualifications.

Owns the roadmap and lifecycle of supply chain data products, from discovery and prioritization through launch, adoption, and outcome measurement. Defines data concepts, KPIs, business rules, quality standards, and operational requirements while partnering with engineering, analytics, architecture, and supply chain stakeholders. Uses Databricks and lakehouse concepts to support analytical and operational workflows involving inventory, replenishment, logistics, fulfillment, transportation, and exception management. Requires occasional travel to operational facilities.

Leads creative deliverables for assigned workstreams, overseeing and producing visual designs, branding, layouts, graphics, style guides, and creative briefs. Contributes to information architecture, taxonomy, and content structures while directing project delivery and offshore resources. Requires extensive enterprise digital design experience across web and mobile, strong UI and visual design expertise, UX knowledge, technical communication skills, and advanced understanding of design systems and design tokenization.

Leads web analytics strategy, GA4 and GTM implementations, tagging specifications, dashboards, reporting, experimentation, and data-quality monitoring. Analyzes user behavior, conversion funnels, feature adoption, and A/B test results to provide actionable recommendations for product and business decisions. Partners with product, engineering, UX, and business stakeholders and communicates analytical findings to technical and non-technical audiences.

Analyze business processes, data quality, and datasets to identify AI and automation opportunities. Translate business requirements into AI/ML use cases, collaborate on model development, evaluate model performance, and deliver dashboards and reports. The role also supports data validation, AI solution monitoring, emerging technology research, governance, compliance, responsible AI, prompt engineering, and generative AI initiatives while partnering with business, data science, engineering, and product teams.

Lead development of an enterprise Agentic AI platform, cloud deployment frameworks, and autonomous workflows. Manage 35+ engineers and multiple technical leaders while overseeing architecture, delivery, hiring, budgets, customer implementations, cloud modernization, observability, security, compliance, AI governance, and technical proofs of concept. Collaborate with product, business, executive, and customer teams to align requirements and engineering outcomes across AWS, Azure, and Google Cloud.

Lead technical programs for branded card products, including launches, enhancements, and digital wallet integrations. Manage roadmaps, timelines, budgets, risks, dependencies, and stakeholder communication across engineering, QA, product, risk, compliance, and design teams. Track milestones using Jira dashboards, resolve cross-functional issues, report progress, and improve delivery processes. The role requires strong software development knowledge, Agile experience, database querying skills, cloud familiarity, and understanding of microservices architecture.

Design and implement browser automation and AI-assisted workflow solutions. Build integrations with enterprise applications and APIs, handle authentication, orchestration, exception handling, and improve reliability and performance of automation systems.

Analyzes business processes, data quality, and datasets to identify AI and automation opportunities. Translates business requirements into AI/ML use cases, collaborates on model development, evaluates model performance, and creates dashboards and reports. The role monitors AI solutions, researches emerging technologies, and supports governance, compliance, responsible AI, and prompt engineering initiatives while partnering with data scientists, engineers, product teams, and business stakeholders.