Top Data & Analytics Jobs

Conducts deep-dive market analyses covering supply, demand, pricing, competition, regulation, and growth trends. Builds market models, forecasts, dashboards, and executive presentations from public, industry, customer, and internal data. Identifies strategic opportunities, risks, sustainability impacts, and value creation possibilities, then communicates recommendations to senior leadership. The role serves as commercial-team bench strength with a potential path into account management.

Lead Zip’s Business Intelligence and GTM Data Science functions, establishing enterprise data strategy, governed semantic models, dashboards, executive reporting, and AI-ready data foundations. Partner with Finance, Revenue Operations, Product Data Engineering, and GTM teams to create a single source of truth for SaaS metrics. Build and lead a high-performing team of analytics engineers, BI developers, and data analysts while supporting executive, board, and investor decision-making.

Leads qualitative and quantitative market research to uncover customer, market, competitive, and healthcare insights. Designs and manages studies, analyzes data using statistical and AI-assisted tools, develops reports and dashboards, presents recommendations, maintains research quality, and coordinates external partners. Collaborates with product, engineering, sales, marketing, and customer success teams to influence product strategy, growth, innovation, and customer experience initiatives.

Supports data preparation, organization, validation, quality control, analysis, reporting, and data governance activities. Assists with data lake development, process documentation, workflow maintenance, and stakeholder collaboration. Participates in projects and meetings to learn how technology and data support business decisions while maintaining confidentiality and following firm policies.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Develop and support enterprise business intelligence, data warehouse, ETL, reporting, dashboard, and analytics solutions. Build optimized SQL and PL/SQL queries, troubleshoot applications and data issues, support system interfaces and testing, participate in deployments, and provide on-site technical support to government users and senior leadership. Maintain documentation, perform root cause analysis, and implement improvements for system reliability and performance.

Supports the Army’s cPROBE data repositories and database layer by designing, implementing, and maintaining reliable, secure, high-performance data infrastructure. Responsibilities include managing interfaces and APIs, minimizing data access latency, defining data and metadata requirements, designing data warehouses, implementing business rules, facilitating change control, and conducting data verification and testing. The role also coordinates with architects, programmers, analysts, and engineers while providing product support and maintenance.

Builds and maintains business development dashboards, datasets, reports, automated workflows, and AI-enabled internal tools. The role analyzes pipeline and revenue performance, develops research-backed briefs, improves existing systems, and identifies new data solutions. It partners with IT, Data, and AI teams, translates stakeholder needs into technical requirements, presents findings to leadership, and mentors colleagues on analytics and tooling.

Design and own a unified data model for an AI-driven pipeline risk platform. Responsibilities include lakehouse and medallion architecture, geospatial modeling, entity resolution, ingestion patterns, governance, lineage, data quality, access controls, and traceability. Partner with data engineers, application engineers, and data scientists to implement schemas, pipelines, serving layers, and optimization strategies while maintaining clear architecture documentation.

Designs and owns analytics-ready datasets, semantic views, ELT pipelines, and standardized metrics in Snowflake. Partners with business, analytics, and engineering teams to enable self-service BI, maintain data quality and documentation, and support forecasting, machine learning, and generative AI initiatives. Provides senior guidance on data modeling, analytics engineering practices, and scalable data product design.