Collaborate with AI tools to generate actionable business insights that support strategic and operational decision-making
Review, validate, and continuously improve the context and inputs provided to analytics AI systems to enhance output quality and relevance
Support the organization's transition to an AI-native ELT (Extract, Load, Transform) pipeline by evaluating, implementing, and documenting emerging data engineering technologies
Assist with the adoption of full CI/CD (Continuous Integration/Continuous Deployment) governance practices for data management, including pipeline testing, version control, and deployment workflows
Review AI-developed analytical projects and outputs for accuracy, completeness, and business alignment; identify errors and recommend improvements
Coach and upskill internal stakeholders on how to effectively interact with AI tools to improve the quality and usefulness of their analytical outputs
Monitor data infrastructure performance and contribute to efforts that reduce organizational risk through reliable, well-governed data practices

Requirements

Bachelor's degree in Data Science, Analytics, Statistics, Computer Science, Information Systems, or a related field
Equivalent combination of education and hands-on experience in data analysis or data engineering may be considered
3–5+ years of experience in a data analyst, analytics engineer, or related data role
Demonstrated experience using SQL for data querying, transformation, and analysis in production environments
Experience with ELT/ETL pipeline concepts and tools, including exposure to modern data stack technologies (e.g., dbt, Fivetran, Airbyte, or similar)
Familiarity with CI/CD principles and version control practices as applied to data workflows (e.g., Git, GitHub Actions, or equivalent)
Experience working with or alongside AI/ML tools for analytical purposes, including prompt refinement, output validation, or AI-assisted insight generation
Experience in healthcare, telehealth, or regulated data environments
Proficiency in SQL for complex querying, joins, aggregations, and data modeling
Strong working knowledge of Excel and/or Google Sheets, including advanced formulas, pivot tables, and data visualization
Proficiency in Python for data manipulation, automation, and analysis (e.g., pandas, NumPy, or similar libraries)
Familiarity with AI-native analytics tools and prompt engineering techniques for business intelligence applications
Working knowledge of ELT pipeline architecture and data transformation frameworks
Understanding of data governance, data quality best practices, and CI/CD principles for data management
Ability to communicate technical findings and data insights clearly to non-technical stakeholders
Must be legally authorized to work in the United States
Must reside within the United States.

Benefits

We’re a fast-growing, fully remote healthcare organization on a mission to improve access to care
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLELTETLPythondata manipulationdata analysisdata modelingdata transformationdata governancedata quality

Soft Skills

communicationcoachinganalytical thinkingproblem-solvingcollaborationattention to detailstakeholder engagementcontinuous improvementadaptabilityorganizational skills