Top Hybrid Data & Analytics Jobs

The Associate Manager, Risk Operations will support an asset management client, focusing on investment data systems, data validation, dashboard creation, and research for investment strategies.

Own and define the technical direction of Dubsado’s data infrastructure as its first dedicated data hire. Assess and improve existing pipelines, warehouse systems, analytical models, reliability, quality, and observability. Build scalable data infrastructure, establish engineering standards and guardrails, support AI and customer-facing data features, and collaborate with engineering, product, and business stakeholders. Over time, expand contributions to backend services, infrastructure, and agentic systems while distributing data ownership across engineering.

Own WireScreen’s robotics industry coverage as an internal and external subject-matter expert. Research and map global robotics subsectors, key players, technologies, supply chains, and industry developments. Monitor conferences and publications, engage with experts, collaborate with product and engineering teams to improve platform coverage, and write reports for customers and internal stakeholders.

Leads Vitana’s data and technology strategy by building centralized data architecture, warehouses, pipelines, integrations, reporting, and governance. Serves as the technical leader across IT infrastructure, vendors, security, compliance, and technology implementations. Partners with executives and practice leaders to translate business needs into scalable solutions, improve data quality, support practice integrations, and connect technology investments to operational and financial outcomes.

Leads commercial real estate research, analytics, forecasting, risk analysis, and market intelligence using diverse economic, financial, lending, and real estate data. Oversees research tools, models, databases, and quality processes; communicates findings through reports, presentations, webinars, podcasts, and client materials. Mentors team members, partners with Client Services, and standardizes analytical and reporting practices across the Research Group.

Lead marketplace and product analytics for Lyft’s Central Market Management organization. Develop analytical frameworks, business models, and recommendations that influence strategy, product roadmaps, and growth. Analyze trends, identify opportunities, communicate insights to senior leaders, and partner with Data Science, Product, Engineering, and other teams. The role requires strong SQL, data analysis, strategic problem-solving, storytelling, and cross-functional influence.

Designs and evolves the organization’s data warehouse, semantic layer, transformation pipelines, data products, and reusable analytics assets. The role implements data governance, quality testing, monitoring, alerting, documentation, and scalable data models across cloud platforms. It partners with data engineering, analytics, research, and governance stakeholders to support self-service analytics and AI readiness while influencing long-term data platform strategy.

Lead actuarial analyses, evaluate statistical methods, stress-test and validate internal cyber risk models, investigate data quality, interpret findings, and communicate results to internal and external stakeholders. The role also supports research and development, underwriting and actuarial tool enhancement, documentation, and analytical assistance for team members.

Administer and support SQL Server and cloud databases for a tolling environment. Responsibilities include database monitoring, installation, upgrades, patching, migration, performance tuning, troubleshooting deadlocks and blocking, resolving application connectivity issues, supporting high availability, and managing very large databases. The role also provides help desk and application support, uses operating-system scripting, and recommends database improvements while collaborating with engineering and operations teams.

Own production-grade analytics data products, including reusable dbt models, dimensional models, metrics, access controls, tests, documentation, and Lightdash guidance. Partner with stakeholders across GTM, Finance, and data teams to define requirements, reconcile conflicting source data, validate results, and publish trusted metrics. Diagnose data quality issues, collaborate with data engineering, review modeling changes, and establish shared standards. Regularly use and evaluate AI coding agents in analytics workflows.