Data Engineer II

About this role

We’re Scan.com http://Scan.com, the digital health scale-up making diagnostics accessible, fast, and transparent. Our technology speeds up diagnoses for timely treatments, improving healthcare outcomes for hundreds of patients each day. We're doing diagnostics differently, with solutions tailored to both patients and providers, all backed by our technology and world-class customer operations team. Our B2C marketplace simplifies booking a scan, making it as straightforward for patients as booking a hotel. Our B2B platforms provide live scheduling at the point of care and harness AI to ease workflows for physicians, attorneys, and providers. WHAT YOU WILL BE GETTING INVOLVED IN Data at Scan.com http://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 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. This can mean writing a Python ingestion pipeline or modeling a new source - Maintain data quality standards across the warehouse: write dbt tests, monitoring for anomalies, and ensuring 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 various data science or data engineering projects based on business demand and based on your own personal development: pipeline development, ML feature preparation, or predictive modeling. 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 out your input before they finalize 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. Ingestion pipelines with libraries like dlt, pandas-based transformation scripts, or scripted automation of manual reporting processes - For this role, the expectation is that you are an AI-Native. You build with AI directly, shipping, automating, or prototyping real work with L