Scan.com is transforming diagnostic imaging, a $500B+ global market, by rebuilding its outdated infrastructure from the ground up. Having built the UK’s largest marketplace for self-pay MRI scans, we’re now scaling at breakneck speed to become the world’s leading medical imaging platform.

Our mission is clear: make medical imaging radically accessible—because early, efficient diagnostics save lives. Inefficiency shouldn’t cost lives.

By integrating fragmented imaging providers, payers, and technology into a seamless, modern ecosystem, we’re creating an effortless, patient-first experience that improves healthcare outcomes globally.

Backed by top-tier VCs across the US & Europe, we’ve raised $50M+ to date and are just getting started.

Join us as we redefine the future of medical imaging.

We're looking for a Data Analyst at an exciting time — we've successfully launched multiple platforms and products, raised over $100M in VC funding, reached profitability, and have a growth trajectory of over 100% YoY.

WHAT YOU WILL BE GETTING INVOLVED IN:

Data at Scan.com is a full-stack discipline. We don't have analysts who hand off to engineers to build the model, or engineers who hand off to analysts to build the dashboard. You will own the work end-to-end — from raw source data through the transformation layer, through the product that a stakeholder uses to make a decision.

Our data team supports every function across the US and UK: operations, revenue cycle, marketing, sales, provider success, and product. We sit at the center of a fast-moving business, and the work ranges from answering a precise operational question to building the automated reporting infrastructure that makes that question answerable without us in the loop next time.

We use a modern tech stack: Fivetran and Python for ingestion, Snowflake as our data warehouse, dbt for transformations, GitHub for CI/CD, Tableau for visualizations, and Cursor, Claude, and Snowflake Cortex for AI-assisted development. Our source systems span Postgres, HubSpot, Front, Acuity, Dialpad, and Facebook and Google Ads.

As a scale-up business, you can expect your role to develop over time. Here are some of the types of things you could be getting involved in:

Build and maintain dbt models that transform raw source data into reliable, well-documented, tested analytical assets used across the business

Partner with stakeholders across Operations, Finance, Marketing, Sales, and Product to translate ambiguous business questions into precise analytical deliverables. You should be able to identify if the ask needs refinement before the work begins

Design and ship Tableau dashboards and data products that automate reporting that currently requires manual effort, permanently removing recurring analytical burden from the team

Identify gaps in our data coverage and work with engineering or independently to close them — whether that means writing a Python ingestion pipeline or modeling a new source

Maintain data quality standards across the warehouse: write dbt tests, monitor for anomalies, and ensure that the numbers stakeholders see are trustworthy

Contribute to the team's analytical infrastructure. This means shared macros, source definitions, documentation standards, and CI/CD practices.

Stretch into data engineering or data science as business needs demand and for your own personal development: pipeline development, ML feature preparation, or predictive modeling are all on the table for the right candidate

THE TOP 5 THINGS WE WANT YOU TO ACHIEVE IN YOUR FIRST YEAR:
Deep business context. Within 90 days, you understand the unit economics, operational workflows, and key performance drivers across the functions you support. You can receive a stakeholder request and immediately identify whether it's answerable with existing models, requires new modeling work, or requires a better-defined question.

Reliable analytical infrastructure. You have meaningfully improved the coverage, test depth, and documentation of our dbt model layer — making it easier for the next analyst to build on your work and reducing the frequency of data quality incidents.

High-impact data products shipped. Several Tableau dashboards or analytical products are in production and actively used by stakeholders to make decisions, replacing manual reporting or filling a genuine analytical gap.

Automation wins. You have identified and eliminated at least one recurring manual reporting process, replacing it with a scheduled, automated output that runs without human intervention.

Trusted analytical partner. Stakeholders across the business seek your input before finalizing requirements, not after. You are known for asking the right clarifying questions and delivering work that answers the real question, not just the stated one.

WHAT YOU MIGHT BRING TO THE TABLE:

You don't need to tick all the boxes to apply for this role. Whether it's your first role or your fifth, we believe everyone can add value, learn, and grow. However, these might be some of the ways you are currently adding value:

Strong SQL: you write complex queries fluently, understand query performance, and don't need a template to construct a multi-stage transformation

Hands-on experience with a modern data transformation tool, ideally dbt or SQLMesh. You understand the model DAG, have written tests and documentation, and are familiar with CI/CD practices in a transformation layer

Experience with a modern cloud data warehouse, ideally Snowflake or BigQuery — you understand how storage and compute interact and can write warehouse-idiomatic SQL

Proficiency with a BI tool such as Tableau, Metabase, Sigma, or Omni — you can design a dashboard that stakeholders actually use, not just one that technically answers the question

Strong stakeholder instincts: you have worked directly with non-technical stakeholders to scope and deliver analytical work, and you know how to translate between business language and data model logic

Python for data work is a strong plus. Ingestion pipelines with libraries like dlt, pandas-based transformation scripts, or scripted automation of manual reporting processes

Startup experience is a plus. You are comfortable with ambiguity, can prioritize without perfect information, and don't need a fully defined ticket to get started

Healthcare experience is a plus, particularly in imaging, RCM, or provider operations — but strong analytical fundamentals in any high-velocity domain are equally valued