Top Entry Level Data & Analytics Jobs in Denver & Boulder, CO

Reviews and prepares scientific datasets, metadata, documentation, and supplemental files for public archival publication through the Forest Service Research Data Archive. The role validates data quality, formats, accessibility, and compliance with metadata standards across tabular, geospatial, multimedia, database, and document formats. It also supports and trains researchers, coordinates delivery timelines, and assists with project collaboration.

Analyze and maintain manufacturing product data, engineering bills of materials, and product records across PDM systems and Microsoft Dynamics 365. Identify discrepancies in part numbers, attributes, classifications, and BOM relationships; support data standardization, reconciliation, documentation, and corrective updates. Collaborate with engineering and manufacturing stakeholders to resolve data issues and ensure accurate ERP product information.

Owns specialty pharmacy data-partner specification negotiations, data mapping, Ascend platform configuration, internal QA, defect resolution, and UAT readiness across multiple implementations. The role evaluates standard versus custom data requirements, coordinates with delivery, engagement, product, and engineering teams, maintains data dictionaries and implementation documentation, supports schema normalization, and develops repeatable testing and onboarding processes. It is primarily remote with occasional travel.

Leads the migration of legacy data warehouses and pipelines to an Azure and Databricks lakehouse. Defines technical architecture, reusable Azure Data Factory and Databricks pipelines, reconciliation methods, quality gates, and performance solutions. Mentors data engineers, reviews technical work, and collaborates with product, QA, platform, and customers in a remote Agile environment.

Delivers daily, weekly, and month-end customer reporting while maintaining solution components, validating data, and resolving reporting issues. Analyzes complex datasets, supports customer implementations, and improves reporting efficiency using Excel, Access, SQL, and the Infios FAP Portal. Collaborates with Operations, Product, Implementations, Commercial teams, and customers to provide accurate, data-driven outputs and document scalable processes.

Owns performance analytics for General Liability claims processes, monitoring trends, diagnosing root causes, evaluating process improvements, and recommending priorities to leadership. Partners cross-functionally with claims, actuarial, underwriting, data, and field operations teams. Presents metrics and action items, validates deployed solutions, supports process optimization, and translates customer and business needs into actionable data-driven solutions.

Analyzes and interprets data, evaluates results, and provides actionable insights to support strategic decision-making across departments.

Develop and maintain ServiceNow reports, dashboards, performance analytics, data extracts, and Power BI executive reporting. Build data models, KPIs, measures, and visualizations for operational performance, compliance, service health, and engineering effectiveness. Automate reporting, data collection, validation, distribution, and notifications using Power Automate, PowerShell, Python, or similar tools. Support Workspace ONE, Intune, macOS troubleshooting, monitoring, endpoint compliance, incident resolution, documentation, and change management.

Review complex geospatial questions, identify missing information, establish assumptions, and create detailed analytical workflows. Select appropriate data sources, tools, methods, validation steps, and deliverables for real-world applications such as crisis response, agriculture, site selection, and supply chain analysis. QA specialists review completed work and provide feedback against project guidelines. The role is fully remote, freelance, flexible, and project-based.

Analyze banking and financial crime data, develop and tune statistical and machine learning classification models, evaluate model performance, identify data quality issues, and recommend optimization strategies. The role uses Python and SQL for feature engineering, statistical modeling, automation, and large-scale data processing. Responsibilities also include documenting methodologies, reporting results to management and regulators, and collaborating with compliance, technology, business, and model governance teams.