Cloud Data Architect Jobs
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Fraud Data Scientist
Title: Fraud Data Scientist Compensation: $140k - $165k base + bonus Company Summary: A leading consulting firm is seeking a Fraud Analytics Senior Associate/Data Scientist to help clients across financial services, fintech, and payments strengthen fraud detection capabilities. The role combines fraud strategy, machine learning, and advanced analyt...
Lead Data Scientist
Company OverviewGlobal insurance and financial services company with over 90 years of history.Provides personal, commercial, specialty insurance, reinsurance, and asset management services.Operates in more than 100 countries and serves a broad range of customers and industries.SummarySeeking an analytics leader to build machine learning and AI solu...
Kubernetes Tech Lead
Must be based in the Continental United States8+ years of Platform Engineering, Infrastructure Engineering, SRE, DevOps, or Kubernetes Engineering experienceDeep hands-on experience building, scaling, and operating Kubernetes platforms in productionStrong understanding of Kubernetes architecture, networking, storage, security, and cluster lifecycle...
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Data Analyst
Data Analyst | Quantitative Trading FirmOverviewJoin a leading quantitative trading firm where data, technology, and research drive business performance.Work with large-scale financial, market, vendor, and alternative datasets that support critical business and research functions.Partner with engineers, data professionals, and business stakeholders...
Vice President of Engineering, Investment Data & Analytics
Vice President of Engineering, Investment Data & AnalyticsBoston, MA (Hybrid)Position OverviewLead the strategy, architecture, and delivery of enterprise data and analytics platforms that support investment research, portfolio management, and business intelligence initiatives.Partner closely with investment professionals, quantitative researchers, ...
Senior Data Engineer
ResponsibilitiesBuild and optimize scalable data pipelines and data products.Develop reliable datasets for analytics, reporting, and AI applications.Establish data engineering standards, frameworks, and best practices.Improve data quality, monitoring, testing, and platform observability.Integrate and reconcile complex datasets from multiple sources...