Top Hybrid Data & Analytics Jobs

Provide workforce analytics and consulting support to HR leaders across Services and Sales Operations. Develop reporting, staffing, retention, and productivity insights; solve complex business problems with quantitative and qualitative data; create executive-ready narratives; influence analytics and product roadmaps; and advise senior leaders on workforce measures and actionable solutions.

Leads marketing analytics capabilities, including KPI frameworks, recurring reports, executive dashboards, forecasting, predictive modeling, segmentation, and data governance. Translates marketing and business data into recommendations on acquisition, retention, channel performance, ROI, LTV/CAC, and brand health. Directs analytics resources and partners with Marketing, Sales, Finance, and leadership to support data-driven planning, reporting, and strategic decision-making.

Supports Global Distribution by monitoring industry and competitor activity, analyzing investment, performance, fee, flow, and market data, and developing competitive positioning insights. Prepares executive and governance reporting, advises senior sales and business stakeholders, contributes to product and pricing strategy, and partners cross-functionally on growth decisions. Improves and automates research and reporting workflows using approved AI tools while maintaining confidentiality, controls, and compliance.

Establishes and operates North America Operations’ data-governance framework, including ownership, stewardship, quality, metadata, lineage, definitions, and AI governance. Partners with business, data, technology, risk, and compliance teams to resolve data issues, automate controls, improve trust, and embed governance into data products, analytics, and AI solutions. Develops playbooks, training, metrics, and continuous-improvement processes while driving accountability across functions.

Develop and enhance data warehouses, BI platforms, Tableau dashboards, and self-service analytics solutions. Analyze data, fulfill rapid reporting requests, identify actionable insights, automate reporting processes, and collaborate with business, BI, and IT stakeholders to improve data quality and decision-making.

Design, build, test, and maintain cloud-based data pipelines and ETL processes using Python, Databricks and Spark. Develop SQL queries and dashboards, automate workflows, integrate code with GitHub/GitLab CI/CD, troubleshoot data quality, and collaborate with stakeholders to deliver scalable, secure data solutions.

Leads the Americas Business Intelligence Center, managing research leaders and delivering market, competitor, industry, company, and financial intelligence for business development and client delivery. Partners with senior stakeholders, oversees research quality and commercial support, drives responsible generative AI adoption, improves workflows and reporting, manages research vendors and subscriptions, and develops a high-performing team.

Build and enhance business intelligence capabilities through data modeling, reporting, automated data quality tests, and collaboration with data engineering teams. Integrate disparate data sources into warehouse models, support data access and reporting, educate stakeholders on data products, and develop campaign KPIs for clients. The role requires strong SQL, analytics experience, and familiarity with cloud data warehouses, with preferred experience in Python, BI tools, Git, query engines, and dbt.

Leads and mentors data engineering teams while remaining hands-on with technical execution. Responsibilities include designing and operating large-scale data platforms, pipelines, integrations, APIs, streaming and batch processing, and high-volume analytics environments. The manager partners with Product and senior leadership on strategy, removes delivery roadblocks, recruits and develops talent, and advances engineering practices, reliability, security, compliance, and scalability. The role is remote within the United States with up to 10% travel.

Lead a scrum team of 5–7 data engineers to design, build, and operate Azure Databricks Lakehouse solutions. Set technical standards, drive data quality, oversee ETL/streaming pipelines, code reviews, CI/CD, and partner with architects and business stakeholders to deliver scalable, governed data products.