Top Hybrid Data & Analytics Jobs

Lead analysis and insights for sports game titles; translate product and business needs into actionable, data-driven recommendations; build reports and visualizations; develop scalable self-service tools; mentor analysts and partner with Product, Marketing, and Commercial teams to drive player and business outcomes.

Account Executives at LinkSquares manage the entire sales process, focusing on new customer acquisition through research, outreach, and closing. They prioritize teamwork, quotas, and deadlines while driving business opportunities in a collaborative culture.

Lead regional economic intelligence and outreach, conduct qualitative and quantitative research, produce sector and district analysis for policy use, prepare publications and presentations, engage senior external stakeholders, and mentor junior economists.

Fora seeks a Senior Analytics Engineer to enhance data modeling and delivery, ensuring access to reliable insights across teams. Responsibilities include managing the analytics layer, improving data performance, and mentoring while collaborating with stakeholders on analytics needs.

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Supports data engineering through code debugging, troubleshooting, solution development, peer code reviews, and system performance improvements. The intern collaborates with technical teams, reviews documentation, learns emerging technologies, and communicates solutions to team members and business leaders. The role requires current enrollment in a relevant bachelor's or higher degree program, a minimum 3.0 GPA, and programming or version-control experience. The internship is hybrid and lasts 11 weeks.

Supports customer experience and quality initiatives by designing surveys, managing Voice of the Customer processes, analyzing feedback, tracking satisfaction metrics, conducting root-cause analysis, and creating dashboards and executive scorecards. The intern presents actionable recommendations, supports continuous improvement and corrective actions, and documents processes while collaborating with quality, support, engineering, lifecycle services, and business teams.

Own product analytics for Heartflow’s platform by measuring adoption, retention, utilization, and feature impact. Design experiments and quasi-experimental analyses, establish product metrics and event taxonomies, build dashboards, and partner with Engineering on instrumentation. Translate quantitative and qualitative customer insights into product and commercial recommendations. Champion AI-enabled analytics, ensure data quality and compliance in a regulated medical-device environment, present findings to leadership, and mentor analysts.

Analyzes complex automotive data, standardizes datasets and KPI definitions, partners with engineering and governance teams, and develops scalable self-service dashboards. The role translates ambiguous business needs into data requirements, automates digital analytics programs, investigates trends and attributes, and supports enterprise reporting. It requires SQL, Excel, data visualization, statistical analysis, and AI tool experience. The position is hybrid, requiring at least 10 office days monthly, with travel up to 10%.

Leads digital analytics strategy across web, media, CRM, and marketing channels. Develops dashboards, reports, KPIs, forecasts, and actionable insights to improve digital performance, customer engagement, and business growth. Ensures data accuracy, communicates findings to stakeholders, identifies trends and opportunities, and supports analytics training. Requires collaboration across teams and familiarity with digital marketing, e-commerce or lead generation, and analytics platforms.

Analyze exchange datasets across equities, FX, options, and futures; develop liquidity and order-book metrics; and create commercial, client-ready data products. Apply statistical and machine learning methods to identify patterns, anomalies, and predictive signals. Partner with engineering, sales, marketing, internal teams, and clients on data ingestion, normalization, cloud distribution, product specifications, competitive analysis, and market-data commercialization.