Analytics Engineer
Location: On-Site Miami · Reports to: Director, Data & Analytics · Department: Data & Analytics
eMed is a digital-health company built on its Empathetic AI™ Population Health Platform. We partner with large employers, health plans, unions, and government programs to reduce obesity and improve chronic disease outcomes through connected, at-home clinical care — combining remote diagnostics, telehealth, and GLP-1 medication management at scale across the US and UK.
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.
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Design, build, and maintain dbt models across staging, intermediate, and mart layers on our AWS Redshift warehouse, following consistent naming, testing, and documentation standards.
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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.
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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.
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Translate ambiguous business questions and stakeholder requests into well-structured, reusable data models rather than one-off queries.
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Write and maintain dbt tests, documentation, and lineage so definitions (e.g. “active patient,” “adherence”) are consistent, discoverable, and governed across the business glossary.
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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.
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Contribute to data governance and quality initiatives, including the ongoing global data-source QA process across our foundational marts.
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Participate in code review, deployment, and CI/CD practices (Git, dbt, Terraform-managed infrastructure) to keep the warehouse reliable as it scales.
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5+ years of experience in an analytics engineering, data engineering, or advanced analytics role, with real ownership of production data models.
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Strong SQL skills and hands-on experience with dbt (or a similar transformation framework) in a modern cloud warehouse (Redshift, Snowflake, BigQuery, or similar).
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Solid understanding of dimensional data modeling (facts/dimensions, star schemas) and how to design models that scale and stay maintainable.
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Experience with a BI tool such as Tableau, Looker, or Power BI — you understand how downstream consumers will actually use what you build.
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Comfort working with messy, real-world data and a strong instinct for data quality — testing, validating, and documenting rather than assuming correctness.
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Familiarity with Git-based version control and CI/CD workflows for analytics code.
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Clear written and verbal communication — you can explain a modeling decision or a data quality tradeoff to both engineers and non-technical stakeholders.
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Comfortable operating with a fair amount of autonomy and ambiguity in a fast-moving startup environment.
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Experience in healthcare, life sciences, or another regulated data environment (HIPAA and/or GDPR exposure).
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Experience supporting B2B or external client-facing reporting/data-sharing, including eligibility file ingestion.
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Familiarity with Airflow/Airbyte or similar orchestration and ingestion tooling.
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Exposure to Python for data validation, automation, or lightweight pipeline work.
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Experience contributing to a data governance program (glossaries, metric definitions, data contracts).
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Health Care Plan (Medical, Dental & Vision)
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Retirement Plan (401k)
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Life Insurance (Basic, Voluntary & AD&D)
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Paid Time Off
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Short Term & Long Term Disability
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Catered Breakfast & Lunch Daily, Plus Snacks
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Training & Development
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Wellness Resources
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