Top Hybrid Data & Analytics Jobs

Leads business operations analytics, including strategic reporting, reporting-tool development, data integrity audits, operational trend analysis, and consolidation of data sources. Integrates budget and performance data to support financial planning and strategic decisions, streamlines business processes, evaluates system performance, and mentors analytical teams. Requires independent judgment and availability for nights, weekends, and variable schedules.

Design, build, and maintain scalable data pipelines and ingestion frameworks across cloud platforms. Ensure data quality, lineage, storage, and consumption methods. Implement solutions on Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate with cross-functional teams, and contribute to internal tooling to support analytics and customer-facing data products.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Designs and maintains compliance reporting solutions, dashboards, data extracts, visualizations, and automated workflows. Analyzes data, validates outputs, improves data quality, identifies process enhancements, and delivers actionable insights using BI and analytics tools. Partners with compliance stakeholders and cross-functional teams on solution development, testing, deployment, documentation, UAT, production support, and risk management initiatives.

Conduct independent, high-complexity analytics for Liberty Mutual’s Embedded Channel. Apply advanced statistics, predictive modeling, A/B testing, and diverse data sources to optimize funnels, questionnaires, and underwriting defaults. Build analytical tools, manage complex research projects, synthesize findings into actionable recommendations, and present insights to senior leadership to improve profitability, growth, and customer satisfaction.

Supports Mastercard’s ProCODE team by transforming fragmented enterprise data into operational insights and automation. The intern will develop Power BI dashboards and visualizations, work with Microsoft Fabric and data agents, use Python for analysis and automation, query large datasets with SQL, and apply DAX to translate complex data into actionable business insights. The role includes mentorship, networking, professional development, and exposure to innovative technologies.

Leads enterprise analytical tool validation, including framework refinement, project execution, methodology risk assessment, technical research, and prototyping of industry practices. Communicates findings through reports and presentations, partners with cross-functional leaders to influence strategy, and coaches junior staff. The role also manages multiple concurrent projects and evaluates statistical and econometric models, data governance, and predictive analytics approaches.

As a Decision Analytics Associate Consultant, you will develop statistical models, apply analytic techniques, and communicate results to clients. You will also design custom analyses and guide team projects while mentoring Associates and maintaining client relationships.

Own and evolve business-critical data products, designing scalable data services, APIs, models, and batch or streaming pipelines. Lead projects through implementation, deployment, monitoring, and production support while applying AI tools throughout the software development lifecycle. Collaborate across engineering, product, data, and business teams, contribute to architecture and governance, improve reliability, and mentor earlier-career engineers.

Design, develop, and support enterprise ETL workflows and data pipelines using Informatica PowerCenter and PowerExchange. Process mainframe data, implement transformations and business rules, optimize SQL, and work with relational and NoSQL databases. Monitor and troubleshoot ETL jobs, validate data quality, maintain technical documentation, and collaborate with business analysts, QA, and application teams to translate requirements into technical solutions.

Build and operate scalable growth data pipelines, models, feature datasets, and measurement systems supporting paid acquisition, ad bidding, experimentation, attribution, and audience targeting. Partner with Growth, Marketing, Analytics Engineering, and Data Science to deliver reliable data products, productionize machine-learning workflows, maintain data quality and observability, and improve platform performance, cost efficiency, and developer experience.