Analytics Engineer

eMed, LLC•Miami, FL

46d•Onsite

About The Position

eMed is a leading digital health company specializing in cardio metabolic health through managed GLP-1 programs. Focused on delivering for employers and benefits administrators seeking to control skyrocketing healthcare costs driven by obesity-related conditions. Our integrated approach — combining telehealth, medication management, coaching, and outcomes tracking — makes eMed a differentiated and proven solution. Delivering structured programs that make GLP-1 therapy safe, sustainable, and cost-effective. eMed solution empowers both the employer and employee to take control of their healthcare costs and their health. We're looking for an Analytics Engineer to join our Global Data & Analytics team and help build the data models, pipelines, and semantic layer that power reporting and decision-making across eMed's clinical, product, marketing, and B2B client operations. You'll sit at the intersection of data engineering and analytics: taking raw data from our warehouse and shaping it into clean, well-tested, documented models that analysts, executives, and external partners can trust. This role backfills a key seat on the team and offers significant ownership over how eMed defines, governs, and scales its core data marts as the company grows across new markets and B2B accounts.

Requirements

5+ years of experience in an analytics engineering, data engineering, or advanced analytics role, with real ownership of production data models.
Strong SQL skills and hands-on experience with dbt (or a similar transformation framework) in a modern cloud warehouse (Redshift, Snowflake, BigQuery, or similar).
Solid understanding of dimensional data modeling (facts/dimensions, star schemas) and how to design models that scale and stay maintainable.
Experience with a BI tool such as Tableau, Looker, or Power BI — you understand how downstream consumers will actually use what you build.
Comfort working with messy, real-world data and a strong instinct for data quality — testing, validating, and documenting rather than assuming correctness.
Familiarity with Git-based version control and CI/CD workflows for analytics code.
Clear written and verbal communication — you can explain a modeling decision or a data quality tradeoff to both engineers and non-technical stakeholders.
Comfortable operating with a fair amount of autonomy and ambiguity in a fast-moving startup environment.

Nice To Haves

Experience in healthcare, life sciences, or another regulated data environment (HIPAA and/or GDPR exposure).
Experience supporting B2B or external client-facing reporting/data-sharing, including eligibility file ingestion.
Familiarity with Airflow/Airbyte or similar orchestration and ingestion tooling.
Exposure to Python for data validation, automation, or lightweight pipeline work.
Experience contributing to a data governance program (glossaries, metric definitions, data contracts).

Responsibilities

Design, build, and maintain dbt models across staging, intermediate, and mart layers on our AWS Redshift warehouse, following consistent naming, testing, and documentation standards.
Own and evolve core data marts (e.g. patient, prescriptions, clinical outcomes, B2B/eligibility) that feed Tableau dashboards used by Executive, Clinical, Marketing, Product, and B2B stakeholders across US and UK markets.
Partner with Data Engineers to ensure upstream ingestion (Airbyte, source systems) lands in a state that's reliable and analytics-ready, and flag/fix data quality issues at the source when possible.
Translate ambiguous business questions and stakeholder requests into well-structured, reusable data models rather than one-off queries.
Write and maintain dbt tests, documentation, and lineage so definitions (e.g. “active patient,” “adherence”) are consistent, discoverable, and governed across the business glossary.
Support B2B client analytics and reporting needs — building or maintaining models behind client dashboards and eligibility/data-sharing pipelines — in coordination with client-facing analysts.
Contribute to data governance and quality initiatives, including the ongoing global data-source QA process across our foundational marts.
Participate in code review, deployment, and CI/CD practices (Git, dbt, Terraform-managed infrastructure) to keep the warehouse reliable as it scales.