Top Remote Entry Level Data & Analytics Jobs in CO

Reviews video annotations, action labels, and hand-pose data for accuracy, consistency, and completeness. Corrects errors using detailed quality guidelines, evaluates English-language descriptions, documents findings, and provides feedback on recurring issues. The role requires methodical visual review, precise corrections, clear written communication, and independent work in a remote environment. No prior AI experience is required.

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

Performs accurate data entry, verification, updating, cleanup, organization, and maintenance across databases, spreadsheets, and digital systems. Maintains electronic records, converts documents into digital formats, prepares basic reports, retrieves information, tracks assignments, and supports scanning and filing. The role requires confidentiality, attention to detail, strong organization, dependable independent work, effective time management, and reliable remote-work capabilities.

Own data partner specification negotiations, data mapping, Ascend platform configuration, and internal QA across multiple implementations. Review file specifications, normalize schemas, validate test files, document data dictionaries, and route non-standard requests to Product or Customer Engineering. Coordinate with delivery and engagement teams on UAT, defects, readiness, and capacity constraints. Develop repeatable QA frameworks, implementation playbooks, procedures, and training materials while ensuring configurations meet quality standards before go-live.

The Database Administrator III role requires U.S. citizenship and completion of all work within the continental United States, Alaska, or Hawaii. The provided description does not specify database administration responsibilities, tools, or technical qualifications. Compensation is $120,000 to $150,000 annually.

Advances the firm’s clinical analytics platform, translating pharmacy and utilization-management expertise into scalable analytical frameworks. Partners with financial, analytics, clinical, and payer teams to standardize insight generation, validation, and communication. Develops pharmacist training, mentorship, educational materials, and enterprise clinical guidance while supporting specialty-drug, medical-pharmacy integration, and outcomes-focused strategy.

Collect, clean, validate, and analyze operational data from multiple systems for customer reporting and business insights. Perform SQL-based processing and data uploads, identify data inconsistencies, maintain reliable datasets, document procedures, and develop standardized workflows. Support dashboards, reporting tools, process improvement, and automation initiatives while collaborating with cross-functional teams and traveling to customer sites.

Collect, clean, validate, and analyze operational data from multiple systems to support customer reporting and performance monitoring. Process SQL-based uploads, maintain accurate datasets, identify trends and anomalies, document reporting procedures, and create standardized workflows, dashboards, and tools. The role also supports process improvement and automation initiatives while collaborating with cross-functional teams and traveling to customer sites.

Entry-level Data Analyst supporting Performance & Analytics for care operations. Build, validate, and QA recurring reports and dashboards (Retool/BI tools), pull and clean payer, clinical, and operational data, investigate discrepancies, maintain workflows and reporting-ready datasets, and support ad-hoc analytical priorities.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.