Top Hybrid Data & Analytics Jobs

Develops and maintains database solutions supporting client marketing objectives, including SQL scripts, stored procedures, ETL feeds, data warehouses, automated reporting, campaign preparation, and quality-control processes. Performs code and data audits, troubleshooting, process documentation, data extraction, and production enhancements. Requires strong database development, programming, analytical, documentation, and collaboration skills.

The Developer, Data and Integration maintains and develops scalable integration solutions focusing on B2B/EDI transactions, working with business units to translate data needs into technical solutions, and ensures the reliability and performance of data flows.

Leads cross-functional data management and analytics projects for pharmaceutical and life sciences clients. Responsibilities include data warehouse development, ETL and schema design, data analysis, process optimization, client communication, project management, business development, and mentoring junior staff. The role also provides strategic guidance, develops reporting solutions, manages timelines and resources, and contributes to internal data platform initiatives.

Own analytics for Customer Success and Operations by translating ambiguous business questions into metrics, reports, dashboards, and PRDs. Partner with senior leaders, CSMs, Analytics Engineers, and Operations teams to improve retention, permit turnaround, and organizational scalability. Build self-service dashboards, develop dbt models, document analytics assets, prioritize high-impact requests, and use LLM tools to accelerate SQL, reporting, and data-model development.

Lead complex quantitative and qualitative consumer research projects for SEGA’s game portfolio from study design through analysis, synthesis, reporting, and stakeholder readouts. Partner with Product, Brand, Corporate Planning, and Publishing teams to translate business questions into actionable insights. Manage research roadmaps and budgets, ensure methodological quality, align stakeholders, and mentor junior researchers. Research areas include market sizing, segmentation, concept testing, playtests, pricing, messaging, brand health, and product tracking.

Manage data transformation programs, develop strategies, and maintain client relationships for technology advisory in AI and analytics. Lead diverse teams and ensure program execution meets quality standards.

Analyze survey and secondary data using statistical and multivariate techniques. Clean, structure, code, and summarize datasets; use SPSS, R, Excel, Python, and VBA to conduct analyses and automate procedures. Prepare Excel outputs and PowerPoint presentations, explain findings to internal and external clients, support analytic planning, and ensure accurate, timely delivery of market research work.

Build and lead an applied analytics function that consolidates insurance data, creates trusted sources of truth, improves operational efficiency, and structures data for AI applications. The role owns solutions end to end across architecture, modeling, testing, governance, and production deployment using Snowflake and Azure. Initially hands-on, the leader will partner with Data Engineering and business teams, then hire and mentor data engineers, analytics engineers, and analysts while demonstrating measurable business impact.

Leads a business analytics team supporting revenue-management pricing decisions. Translates ambiguous business questions into hypotheses and analytical solutions, analyzes large structured and unstructured datasets, oversees A/B testing, and delivers recommendations on revenue, occupancy, yield, and pricing strategy. Manages senior analysts, prioritizes projects, coaches team members, and communicates findings to senior executives and revenue-management stakeholders.

Leads a business analytics team supporting cruise revenue management and pricing decisions. Translates ambiguous business questions into analytical objectives, hypotheses, and actionable recommendations using large structured and unstructured datasets. Oversees A/B testing and causal analysis to optimize revenue, occupancy, and yield. Partners with senior revenue-management stakeholders, presents findings to executives, prioritizes projects, coaches analysts, and manages team performance and development.