AI Data Analyst / Data Engineer - 100% Remote
About The Position
We’re a fast-growing, fully remote healthcare organization on a mission to improve access to care—and we know our people make that possible. As we expand, we are adding a new role to our team. We are seeking an AI Data Analyst / Data Engineer who is responsible for accelerating AI-driven analytics and strengthening data infrastructure to reduce organizational risk. About Expressable Expressable is a virtual speech therapy practice on a mission to transform care delivery and expand access to high-quality services, serving thousands of clients since our inception in late 2019. We are passionate advocates of parent-focused intervention. Our e-learning platform contains thousands of home-based learning modules authored by our clinical team, helping SLPs empower caregivers to integrate speech therapy techniques into their child’s daily life and improve outcomes. Our mission is to set a new standard in speech therapy by making every caregiver a champion of their loved one’s success. We envision a world where everyone can fulfill their communication potential. This role operates within a fast-evolving data and technology environment where the Data Analyst/Data Engineer is expected to work alongside AI tools to surface business insights while also serving as a quality control layer for AI-generated outputs. The position requires strong analytical judgment, familiarity with modern data engineering practices, and the ability to guide colleagues in effective AI tool usage. Complexity arises from the dual nature of the role: contributing to technical infrastructure transitions while simultaneously delivering business value through timely, accurate analytics.
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
Responsibilities
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
11-50 employees
