Top Remote Entry Level Data & Analytics Jobs in CO

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Reviews and analyzes large business datasets for accuracy, completeness, relevance, privacy, and quality. Conducts document reviews involving PII, identifies data issues and anomalies, provides feedback to support AI training, refines quality assurance standards, and communicates findings to project stakeholders while maintaining data privacy and security compliance.

Provides remote administrative support for home care operations by managing correspondence, scheduling appointments and staff activities, preparing documents and spreadsheets, updating records, entering data, assisting clients and staff, tracking deadlines, and protecting confidential information. Requires strong organization, communication, computer, and recordkeeping skills, with familiarity in Microsoft Office or Google Workspace. Paid training and opportunities for advancement are provided.

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.

Collects, consolidates, cleans, analyzes, and visualizes experimental data from analytical equipment, test rigs, and pilot plants. Performs statistical analysis, anomaly detection, experimental design support, equipment validation, and QA/QC activities. Presents findings to technical leadership, identifies process improvements, and helps extract actionable insights from structured and spectral data.

Develop and support healthcare IT data strategies, governance frameworks, analytics roadmaps, data quality initiatives, metadata management, and enterprise data integration. Coordinate data migration, legacy clinical data integration, and clinical viewer transition efforts. Collaborate with stakeholders on analytics requirements, support dashboards and reporting, advise leadership on performance metrics, and ensure compliance with privacy, security, and governance requirements.

Entry-level data engineering development program involving three rotations, cloud-native data platforms, batch and real-time pipelines, distributed systems, ETL/ELT, data modeling, AI platforms, and enterprise data products. Participants use AWS, Spark, Java, Python, SQL, Snowflake, Kafka, and related technologies while gaining experience with LLM architectures, APIs, microservices, and secure production deployments.

Conduct quality assurance evaluations, root-cause and data analysis, and progress reporting. Serve as an escalation contact and subject matter expert for quality processes, while supporting problem prevention and continuous improvement projects. The role requires strong communication, analytical, organizational, presentation, and decision-making skills, along with English and Mandarin proficiency and fintech experience.

Leads R&D data stewardship across metadata, cataloging, data products, profiling, quality, governance, and compliance. Uses Informatica, SQL, Databricks, Python, and PySpark to curate data, automate workflows, define quality controls, and resolve complex issues. Applies AI-assisted stewardship and semantic data concepts while collaborating with technical and business stakeholders. Coaches other stewards, supports audit readiness, and improves stewardship standards and operating practices.