Top Data & Analytics Jobs

Build trusted data models, define metrics, ensure data quality, conduct analyses, and create dashboards supporting Product, Finance, GTM, executives, and customers. Present actionable insights, connect GA4, Google Search Console, and AI Search data to business outcomes, and develop reusable reporting frameworks. The role requires strong SQL, AI-assisted analytics, customer communication, and comfort operating in a fast-growing startup.

Supports enterprise applications, business systems, data platforms, and business intelligence solutions. Analyzes user needs, defines requirements, develops reports and dashboards, troubleshoots system issues, maintains security roles, documents processes, and coordinates projects with internal teams and external vendors. The role focuses on Microsoft Dynamics 365 CRM, data analysis, enterprise application support, process improvement, and translating business requirements into effective technology solutions.

Analyze market and live data to set dynamic ticket prices using Uptick, build strategic pricing recommendations, communicate with clients about strategy and performance, and collaborate with BI and engineering to improve product performance.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.

Produce Salesforce-based sales analytics and reporting to improve forecast accuracy, pipeline visibility, territory coverage, and sales productivity. Build dashboards, track conversion and cycle metrics, maintain CRM data quality, support quota and territory planning, and present insights to sales leadership.

Deliver strategic reporting and insights to executives and the Board; develop and track KPIs; build dashboards and visuals; perform ad hoc and project-based analyses; support forecasting, financial modeling, and data stewardship across the organization.

Performs data mapping, transformation, validation, and quality assurance across higher education SIS, LMS, and ed-tech schemas. Builds unified data models, writes data dictionaries and mapping specifications, prioritizes fields, tracks data lineage, and applies transformation logic. Collaborates with internal and external stakeholders to ensure integrated data meets operational needs. Uses SQL, Python, dbt, and Great Expectations while managing project timelines and deliverables.

Leads enterprise information and knowledge management for Joint Staff J-7, developing SharePoint and Microsoft 365 collaboration environments, Power BI dashboards, operational common pictures, KPI scorecards, data visualizations, Power Apps, and Power Automate workflows. Integrates enterprise data into executive decision-support products, applies AI capabilities, trains personnel, and ensures compliance with DoD cybersecurity, records management, accessibility, and information governance requirements.

Build and govern semantic models in Snowflake using SQL, tests, and documentation. Translate finance, operations, and project-controls logic into auditable dimensional models; migrate legacy reporting logic; maintain models as source systems change; and resolve metric definitions with business stakeholders. The role requires strong SQL fundamentals, analytical rigor, curiosity, and communication skills. Early-career candidates are welcome, with senior engineers available for mentorship.

Designs, builds, and maintains university data pipelines, integrations, data models, and analytics-ready datasets. Transforms operational data into trusted, governed assets; implements data quality checks, documentation, security, and access controls; and supports reporting, self-service analytics, AI-ready workflows, and institutional decision-making. Troubleshoots platform issues, automates manual processes, and partners with technical and university stakeholders to improve data delivery.