Top Data & Analytics Jobs in CO

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Designs and maintains scalable data pipelines, integration workflows, data lakes, and engineering platforms using AWS, InterSystems Ensemble, HL7, CCDA XML, and MySQL. Responsibilities include transforming and validating clinical data, optimizing queries, supporting analytics, solving complex client data challenges, and mentoring junior engineers. The role also involves client collaboration, critical thinking, Agile delivery, and adherence to professional and technical standards.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Transform data into actionable insights by building dashboards, reports, and visualizations. Partner with cross-functional teams and data engineering to ensure data quality, lineage, and AI-ready metadata. Audit datasets, define monitoring rules, document data dictionaries, pilot AI-assisted BI tools, and apply hypothesis-driven analysis to surface insights that inform business decisions.

Own the company’s metric registry and canonical definitions, resolve conflicting metrics, enforce definitions in semantic and catalog systems, and retire redundant reporting assets. Build data quality checks, route failures to accountable owners, and evaluate AI agents against ground truth. Enable trustworthy self-service analytics through governed data. The role requires strong SQL, production ownership of a semantic or metrics layer, data catalog or lineage experience, clear documentation, and cross-functional decision-making.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

Supports the Mobile Device Data and Operations team by collecting, cleansing, enriching, and distributing product data to catalogs and vendor partners. Develops workflow designs and toolsets using KNIME or Alteryx, Oracle and Snowflake databases, and AWS. Participates in an 11-week full-time internship with professional development, networking, mentorship, and cross-functional activities.

Own CX data infrastructure, including pipelines, data models, governance, monitoring systems, and Streamlit dashboards. Apply advanced statistics and AI-assisted anomaly detection to identify customer experience opportunities and performance issues. Partner with CX leaders, data science, engineering, and Salesforce teams to define metrics, answer causal questions, and guide operational decisions. Establish data quality standards, validate AI-generated outputs, communicate insights clearly, and mentor data practitioners.

Develop and maintain dashboards, reports, scorecards, KPI solutions, and analytical models that provide business performance insights. Responsibilities include trend and root-cause analysis, forecasting, data validation, stakeholder requirements gathering, reporting automation, process improvement, documentation, and data quality governance. The role partners with cross-functional teams and communicates findings to technical and non-technical audiences.

Build and maintain production data pipelines, Airflow DAGs, Snowflake infrastructure, and Data Vault 2.0 models. Ensure data quality, observability, security, governance, reliability, and cost efficiency through automated testing and CI/CD. Lead technical projects end-to-end, collaborate with stakeholders, and support SOX-relevant reporting while developing expertise in commercial insurance data and processes.

Leads healthcare analytics projects using clinical, claims, pharmacy, and eligibility data to evaluate cost, utilization, quality, and program performance. Develops statistical analyses, predictive models, dashboards, and reporting solutions; identifies optimization opportunities; partners with clinical, product, engineering, and business teams; presents recommendations to stakeholders; and mentors junior analysts.