Top Senior Level Fintech Data & Analytics Jobs in San Francisco, CA

The Senior Data & Analytics Consultant will lead data insights for the pet business's pricing and performance, manage analytics initiatives, and ensure accurate data reporting.

Responsible for managing client data health, ensuring quality and compliance of contributed datasets, and executing strategies for data governance and operational performance improvements in financial services.

Lead quantitative research in macro investment strategies, develop and implement investment ideas, integrate datasets, and present findings effectively.

Lead cross-functional strategic initiatives by breaking down ambiguous problems, conducting deep analyses, designing and evaluating experiments, surfacing actionable insights, and partnering with Product, Data Science, Engineering, Design, and Finance to drive key business metrics and roadmap decisions.

The Strategy & Analytics Senior Lead will drive product decisions through analysis, partnering with cross-functional teams to generate insights and enhance operational processes.

The Director of Analytics Engineering will develop strategies for the analytics platform, manage a team, and collaborate with stakeholders to deliver data solutions at Flex.

The Lead Data Scientist will build pricing systems, lead experiments, mentor teammates, and influence strategy across various departments based on data-driven insights.

As a Senior Data Analyst, you'll manage analytics lifecycles, build dashboards, write SQL and Python code, collaborate with teams, and mentor junior analysts.

The Sr Growth Analytics Manager drives customer acquisition through advanced analytics, optimizing marketing investments, conducting experiments, and collaborating across teams.

The Sr. Systems Analyst collaborates with scrum teams to build Visa's dispute platform by managing requirements, ensuring effective communication, and supporting technical design and testing.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.