Top Data & Analytics Jobs

Own and evolve the Collibra meta-model, integrations, and workflows. Design configurations and security/lineage standards, automate workflows, coordinate changes with Pfizer Global Supply, perform risk/impact assessments, and support development/test/production environments to make trusted data discoverable and usable.

Lead and scale a casino analytics team to drive data-informed strategy, experimentation, and reporting. Partner with product, data science, and operations to deliver executive insights, build analytical frameworks, and develop high-performing analysts to unlock growth and operational efficiency.

Analyze administrative and clinical healthcare data to build reporting solutions, identify optimization opportunities, and support clinical program effectiveness. Apply statistical analysis, data mining, and predictive modeling to improve decision-making, patient outcomes, utilization, and cost efficiency. Develop dashboards and KPIs, monitor healthcare trends, collaborate with clinical, data science, engineering, and client teams, and lead complex multidisciplinary projects.

Collects, audits, and analyzes investment fund data to improve database quality and product accuracy. The role resolves data issues with internal teams, external providers, and clients; communicates data requests and submission instructions; identifies opportunities through data queries; and partners with development teams to create analytical tools that streamline collection and quality assurance. The analyst also executes projects improving efficiency, data quality, and client experience while collaborating with global colleagues.

This role involves analyzing credit risk, improving lending policies, preparing loan performance data, and supporting regulatory compliance efforts. It also includes data querying and collaboration with analytics teams to optimize credit strategies.

The Product Analyst analyzes member and product performance, collaborates with teams to improve products, and provides actionable insights using data.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Finance Data Science and Automation Interns build data pipelines, improve data quality and governance, automate reporting, apply AI and advanced analytics, support financial reconciliations and forecasting, and create Power BI dashboards. They partner with Finance and cross-functional teams to develop actionable insights, executive presentations, financial models, and process improvements. The internship is full-time for 12 weeks in Plano, Texas, and requires active full-time master’s enrollment.

Lead strategy, architecture, and delivery of enterprise data engineering on the Data Cloud Platform. Design batch and real-time pipelines, event-driven streaming, data products, governance, lineage, quality, and observability. Provide technical leadership, mentor teams, drive adoption of standardized data models, CI/CD, and align platform capabilities with business and investment use cases.

Lead analytics and reporting for alternative credit portfolios: monitor and analyze investment, portfolio and fund performance; produce monthly/quarterly/ad hoc client reports; maintain data integrity and reporting processes; support investor due diligence and custom requests; coordinate with valuation, fund accounting, investor relations, compliance and portfolio managers.

Prepare and maintain portfolio and fund-level performance reporting and risk metrics for liquid credit strategies. Produce monthly/quarterly/ad hoc client reports, respond to investor data requests, ensure data and process integrity, and coordinate with valuation, fund accounting, investor relations, compliance, and PMs. Support operational due diligence and new product servicing.

Own the design, governance, and evolution of the enterprise data lake and warehouse. Responsibilities include developing conceptual, logical, and physical data models; defining source-to-target mappings; reconciling legacy CRM and operational data; establishing data quality frameworks; administering schemas, views, access controls, and performance; and partnering with engineering and business stakeholders to support scalable, self-service analytics.

Own website analytics, KPI frameworks, funnel analysis, user behavior insights, and experimentation strategy. Identify conversion opportunities, prioritize roadmap initiatives, and lead testing, optimization, segmentation, and personalization programs. Partner with Marketing, Product, and Engineering to align experiments with business goals, communicate findings to senior leadership, and improve engagement and conversion through accurate measurement and data-driven recommendations.