Top Senior Data & Analytics Jobs

Lead supply chain analytics and AI initiatives focused on MRP accuracy, inventory optimization, production scheduling, and process improvement. Develop statistical, machine learning, generative AI, and reporting solutions; manage supply chain metrics and data governance; optimize SAP MRP parameters; support SIOP planning; and partner with procurement, manufacturing, engineering, and IT teams. Mentor analysts and data scientists while driving automation, analytics strategy, and measurable operational improvements.

Lead data engineering initiatives supporting analytics, machine learning, and business intelligence teams. Design complex data warehouse and data lake models, manage large-scale SQL and Python ETL/ELT pipelines, maintain data quality and lifecycle standards, and translate strategic priorities into actionable projects. Provide technical leadership, mentorship, architectural guidance, project planning, and cross-functional collaboration with product, infrastructure, and architecture teams.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Designs and maintains dashboards, reports, and visualizations; validates data quality and reporting logic; translates complex analysis into actionable recommendations; partners with data teams and stakeholders; improves reporting scalability, self-service analytics, and responsible AI usage. Presents findings to technical and non-technical audiences while supporting strategic and operational decision-making.

The Economist will analyze travel and spending data to identify trends, craft insights, and present findings, serving as an economic advisor to leadership.

Lead advanced risk, fraud, and payment optimization analyses for major fintech merchants. Analyze large-scale transactional, ML output, and labeled risk data; design experiments and statistical investigations; develop scalable analytical tools, ETL workflows, dashboards, and reusable frameworks; collaborate with risk product teams; translate findings into merchant strategies and product improvements; enable commercial stakeholders through playbooks and self-service tools; and mentor analysts.

Build and operate scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Design and administer enterprise dashboards and reporting solutions (Tableau, Power BI, Microsoft Fabric). Translate complex business questions into compelling data stories, partner with stakeholders and BI engineers, champion visualization standards and governance, mentor analysts, and improve reporting usability and trust across the organization.

Architect and own NinjaTrader’s foundational dbt data-model layer, establish modeling standards, and manage paid-media attribution. Develop AI-driven reporting, data-quality automation, agentic workflows, and self-service knowledge tools. Partner with Data and MarTech teams, prevent pipeline issues, scale models across business units and geographies, and mentor teammates while applying marketing measurement concepts such as CAC, LTV, and ROAS.