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

The Quality Assurance Analyst provides technical support to field technicians, audits completed work, analyzes data, and ensures compliance with standards.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

Analyze datasets to generate reports and visualizations that inform business decisions. Produce regular reporting, create data visualizations, and work cross-functionally to support data-driven strategies with attention to accuracy.

The Instructional Designer I creates high-quality online courses, interacts with various teams, and requires strong communication and project management skills.

The Neuroimaging Engineer will analyze MRI data, develop software for algorithms, and assist in managing neuroimaging databases and quality assurance.

General application to Cardamom's talent pipeline for roles across Epic analytics, data & analytics, project/product management, and internal functions. Candidates can submit for Epic analyst, data engineering, data science, interoperability, BI, software development, and entry-level roles (Madison local). Talent team reviews submissions and contacts matches for current or upcoming openings.

Support HRIS implementation by extracting, cleansing, validating, and preparing employee data for migration. Reconcile reports, fix duplicates and missing data, create Excel transformation files, maintain data quality, document work, and assist with testing and post-go-live support.

The Forward-Deployed Data Analyst will engage with African Ministries of Health, build data management solutions, develop dashboards, and support public health decision-making.

Analyze marketing and customer data to identify trends and optimization opportunities. Build scalable lead ingestion solutions, write SQL and Python for ETL and analysis, maintain curated datasets and dashboards, automate reporting, and collaborate with marketing, analytics, data science, and engineering teams while documenting data definitions and methodologies.

Design, build, and maintain scalable ETL/ELT pipelines and data models to populate the data warehouse. Write reusable SQL and Python to transform data, monitor pipeline reliability and quality, enable AI workflows, collaborate with stakeholders, and drive adoption of modern data stack technologies (DBT, Iceberg, Databricks). Support data governance, observability, and security practices.