Top Data & Analytics Jobs in CO

Designs, builds, and maintains scalable ETL/ELT pipelines and finance data models using Snowflake, AWS, dbt Core, Python, and SQL. Develops Medallion architecture data marts, integrates ERP systems, and partners with stakeholders on financial workflows. Implements data governance, RBAC, automated quality testing, observability, documentation, CI/CD, and secure deployments. Monitors Snowflake costs and optimizes queries while supporting Oracle EBS integrations and the migration to Workday.

Supports analytics teams by creating reports and dashboards, conducting data extraction, cleaning, validation, and basic analysis. Documents queries and business requirements, translates business needs into specifications, supports projects under guidance, and communicates findings to technical and nontechnical stakeholders. The role also develops foundational skills in data storytelling, governed cloud data platforms, metric validation, and workload organization.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Own the enterprise reporting strategy and Sigma BI platform, including administration, governance, security, data modeling, dashboard development, and self-service reporting. Partner with business and analytics teams to define KPIs, create root-cause reporting frameworks, connect operational metrics to business outcomes, and improve reporting consistency. Lead Sigma adoption through training, documentation, standards, and mentorship while integrating modern data and AI technologies.

Serves as an enterprise architecture subject matter expert within a DoD environment. Supports DoD Business Enterprise Architecture initiatives, business process alignment, enterprise strategy, stakeholder analysis, requirements documentation, reporting, and project activities using MEGA HOPEX and Microsoft Office tools.

The Analytics Engineer will analyze sales proof-of-concepts and self-service trials, support growth and product-led sales initiatives, and evaluate pricing and monetization strategies. The role partners cross-functionally, models data in dbt, maintains data warehouses and ELT pipelines, experiments with emerging data and AI tools, and trains teammates on effective data workspace practices.

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.

Extract, transform, validate, and load client and Enverus data into MineralSoft using AI-enabled extraction tools and ETL processes. Configure mappings, run queries and reports, troubleshoot integrations with EnergyLink, and ensure data quality and completeness for internal and client use.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.