Top Hybrid Senior Level Data & Analytics Jobs

Own end-to-end data and analytics for SaaS clients: collect requirements, stand up data infrastructure, migrate reporting to Valon Data Schema, build dbt pipelines, rewrite SQL queries and dashboards, conduct analyses, and support client operations under tight deadlines.

The Data Engineer will design and optimize ETL pipelines, integrate data, ensure data integrity, tune SQL queries, troubleshoot issues, and collaborate with teams to create data solutions.

Partner with GTM to translate business goals into technical requirements, design end-to-end automations and AI-agent workflows, prioritize accounts, maintain data integrity and governance, document data models and processes, and liaise across Marketing, Sales, Success, and RevOps to deliver scalable GTM systems and measure tool ROI.

The Senior Fraud Risk Analyst will analyze fraud patterns, develop detection strategies, and implement controls to protect the lending platform from fraud. Responsibilities include monitoring performance data, evaluating fraud tools, and collaborating with cross-functional teams.

Design and maintain secure data pipelines for the Department of Defense modernization. Collaborate on data integration and ensure data accuracy across AWS environments.

The role focuses on building analytics for infrastructure usage and capacity, integrating data sources, and developing dashboards for cloud cost and efficiency metrics, requiring strong SQL and Python skills.

Lead the Reporting and Visualization team to develop data models, dashboards, and reports, transforming complex data into visual insights for stakeholders like sales and marketing teams.

The role involves owning data science pipelines, delivering client-facing features, driving cross-functional collaboration, and leveraging AI tools to enhance workflows, ultimately transforming retail data into actionable insights.

As a Product Analyst, you will analyze user behavior, influence product strategy through A/B tests, and collaborate across teams to drive insights and product improvements.

Lead the design and evolution of the enterprise data platform, establish standards, mentor teams, and collaborate across departments to deliver high-quality data products.