Top Hybrid Data & Analytics Jobs

The Cloud Data Architect will design scalable data architectures for SaaS solutions, set data standards, collaborate with engineering, and ensure data governance and optimization. Requires advanced SQL and cloud platform proficiency.

The Staff Data Analyst will lead marketing analysis, own metrics, mentor a team, and partner with stakeholders to optimize growth strategies.

As a Data Engineer II, you will build and optimize data pipelines in Databricks, ensure high-quality data delivery, and support generative AI applications. You will collaborate with data scientists and AI engineers to provide reliable data infrastructures and meet business needs.

Create and maintain forecasting tools and datasets to analyze past consumption and predict future sales trends. Partner with Sales, Production, and Marketing to produce monthly forecasts, identify high-risk business, and support demand planning and reporting.

As a Lead Product Data Analyst, you'll drive data-informed decision-making across teams, develop data products, collaborate on metrics, and influence product strategies.

The Sr. Analytics Engineer at Philo is responsible for designing data models, governing business metrics, collaborating with stakeholders, and ensuring data quality and documentation for analytics purposes.

Support sales intelligence by collecting, preparing, and analyzing sales and marketing data; maintain dashboards and data quality; run pipeline and win/loss analyses; translate findings into clear visualizations and reports for stakeholders.

As an Execution Quantitative Researcher at PEAK6, you will analyze algorithm performance and collaborate with engineers to optimize electronic execution algorithms for trading.

Lead product-focused analytics to drive consumer and seller experience: perform deep statistical analyses, design and run A/B and multi-armed bandit experiments, build scalable data pipelines and dashboards, partner with cross-functional teams, and present insights to executive leadership.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.