Top Data Analyst Jobs in Denver & Boulder, CO

The Lead Data Analyst (Network) drives data-driven initiatives, dissects datasets, and provides actionable insights to enhance business performance and network efficiency.

Lead Data Analyst responsible for advanced analytics, real-time dashboards, capacity modeling, and network performance insights. Collaborates with stakeholders to identify inefficiencies, run tests and pilots, and deliver actionable recommendations to improve service provider performance and network operations.

As a Data Analyst II, you will enhance marketing strategies by auditing current data processes, creating dashboard reports, and conducting A/B tests with statistical validation to drive customer engagement and growth.

Build and maintain dbt models, write SQL to investigate and validate data, implement data quality checks, document lineage and metrics, resolve production issues (including on-call), and partner with analysts and business teams to deliver production-ready data products using modern warehouse tooling and AI-assisted frameworks.

The Data Analyst II analyzes data using SQL, Python, and R to provide actionable insights, improve processes, and support decision-making across teams. Responsibilities include developing forecasts, liaising with stakeholders, and presenting findings to executives.

Lead data analysis and reporting for the Trading team, build and maintain dashboards and governed analytics, deliver quantitative research, mentor juniors, and present insights to stakeholders to support decision-making and next-gen trading platform development.

The Data Analyst in Revenue Operations will analyze sales data, build models, and create reports using SQL, Excel, and BI tools to empower business decisions.

As a Senior Data Operations Analyst, you'll manage and analyze data operations, ensuring quality data flows, troubleshooting processes, and collaborating with teams to enhance data integrity and reporting.

Develop and improve TransUnion's IP geolocation technology by analyzing large datasets, implementing automation algorithm improvements, and providing analytic insights. Collaborate with engineers and data scientists, use SQL/R/Python/MongoDB/BigQuery, and communicate findings to nontechnical stakeholders. Hybrid role with regular in-office work.

As a Lead Data Science Analyst, you will develop predictive models, perform statistical analyses, and implement AI-driven solutions to enhance the sales customer lifecycle and drive business insights.

Lead customer analytics to inform acquisition, retention, and reactivation. Build segments, maintain BI dashboards, track marketing KPIs and A/B tests, and partner with cross-functional teams to translate insights into marketing and product strategies.

Analyze digital customer journeys and site behavior to identify friction, explain marketing performance, and provide actionable insights. Maintain dashboards, support A/B and multivariate testing, collaborate with developers on tagging and tracking, and drive optimization to improve conversion and retention.

Serve as a data quality and QA engineer on a federal data platform: build automated AWS Glue/Deequ data quality checks, validate schemas and ETL pipelines, create and run unit/integration tests with 90% coverage, support IV&V and UAT, perform static/security scans, produce test documentation, monitor production ETL, and collaborate with data stewards and IV&V teams.

The Senior Data Analyst will deliver analytics solutions, develop KPIs and dashboards, conduct advanced analysis, and mentor junior analysts while collaborating with stakeholders.

Support warehouse operations by analyzing performance data, building dashboards and reports, automating recurring reporting, validating data, and providing actionable recommendations to improve productivity, labor utilization, inventory accuracy, and service performance. Partner with operations leadership for staffing, forecasting, and continuous improvement through statistical analysis and ad hoc investigations.

As a Data Analyst Intern, you will collect, analyze, and interpret data, create visualizations, and support reporting efforts to drive business decisions.

The Data Analyst will analyze data using Google BigQuery and Generative AI, optimize queries, create visualizations, and ensure data quality for strategic decisions.

The Data Analyst manages alarm system information, updates profiles, performs quality assurance, maintains tracking tools, and assists with training.

Own end-to-end enrollment data lifecycle: process and validate HIPAA 834/X12 files, perform data quality audits, enter and maintain member records, run SQL queries for cleanup, collaborate with engineering and operations to remediate data issues and support compliance reporting (ACA/CMS).

Lead data strategy for Stripe's risk product, design metrics and frameworks, manage data reliability, mentor analysts, and evaluate risk experimentations.

Lead development of scalable enterprise analytics and data stewardship. Perform advanced data analysis, assess data quality and lineage, define cleansing strategies, manage metadata and business glossary, support enterprise data warehouse and analytic product backlogs, and collaborate with cross-functional teams to enable self-service and advanced analytics.

Collect, cleanse, and analyze finance-related data; design and maintain Power BI dashboards and reporting environment; ensure data quality and master data management; support data warehouse and reporting requirements; automate processes using Power Automate and AI tools; collaborate with finance and cross-functional teams to deliver actionable insights and troubleshoot reporting issues.

Run funnel audits and synthesize multiple datasets into exec-ready narratives. Recommend and help launch A/B tests, benchmark against genres and market data, partner with game analysts and ETL engineers to validate and maintain clean data, and provide actionable UA, monetization, and live-ops recommendations to customers.

Partner with marketing to build accurate reports, dashboards, and forecasts from multiple data sources, ensure data integrity, deliver actionable insights, and enable analytics self-service for marketing.

Own BI analytics using a modern data stack to generate insights, build dashboards and self-serve tools, partner with stakeholders, and deploy AI-driven reporting and natural-language query solutions.