Top Senior Data & Analytics Jobs in Denver & Boulder, CO

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Build and maintain sales KPI dashboards, perform recurring and ad-hoc sales performance analyses, model efficiency drivers (churn, LTV:CAC, pipeline coverage), create executive insights, partner with GTM leaders, and automate/reporting processes to scale Sales operations.

Owns RTB House’s performance media measurement strategy across products. Responsibilities include defining measurement taxonomies, evaluating attribution, incrementality, MMM, and MTA methodologies, managing third-party measurement partners, developing testing playbooks, piloting identity and measurement approaches, translating insights into market-facing messaging, and training commercial teams. The role requires hands-on performance media buying or oversight experience and strong ability to communicate measurement concepts, limitations, and results to technical, executive, and commercial audiences.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Lead the architecture and migration of Rula’s data platform to Snowflake and Medallion architectures. Govern warehouse performance and cloud costs, establish data contracts and intake guardrails, and enforce GitHub-based CI/CD testing for data quality and query performance. Scale AI-assisted engineering practices across teams while partnering with executives and domain engineers. The role also supports secure clinical data handling, schema evolution, semantic BI integrations, and production ELT pipeline development.

Analyze observability, digital experience, and business analytics data for enterprise clients to improve website performance and user engagement. Manage relationships with 4–8 clients, conduct ad hoc analyses, configure Dynatrace solutions, create reports and dashboards, present findings to technical and executive audiences, provide training, and influence product roadmap decisions. The role combines advanced data analysis, strategic consulting, customer account management, and executive communication.

Lead design and optimization of high-throughput data ingestion pipelines and Python-based microservices. Improve pipeline performance, reliability, and observability across distributed systems, define unified ingestion architectures, work with Snowflake for analytics, mentor engineers, and collaborate cross-functionally to ensure scalable, fault-tolerant data platforms.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Serve as the primary technical escalation point for managed services data and analytics clients. Troubleshoot and enhance Power BI, Fabric, Azure, and other analytics environments; gather requirements; translate business needs into scalable solutions; support client onboarding; maintain documentation; mentor support staff; and partner with client success and consulting teams. The role also contributes to continuous improvement, training, knowledge sharing, and AI-enabled productivity initiatives.

Analyze administrative and clinical healthcare data to build reporting solutions, identify optimization opportunities, and support clinical program effectiveness. Apply statistical analysis, data mining, and predictive modeling to improve decision-making, patient outcomes, utilization, and cost efficiency. Develop dashboards and KPIs, monitor healthcare trends, collaborate with clinical, data science, engineering, and client teams, and lead complex multidisciplinary projects.