Senior Data Engineer

We are a fast-growing vertical SaaS company leveraging innovation and disruptive technologies to improve consumer experiences, outcomes, and predictability within the plastic surgery industry. Our team thrives on challenges, embraces change, and is dedicated to transforming how the industry operates. Data is a critical product within our company, and we are investing heavily in our data capabilities. Our platform is what aesthetic medicine and plastic surgery practices run their business on, covering scheduling, clinical records, forms and consents, invoicing and payments, patient communications, and the marketing that sits in front of all of it.

It is a multi-tenant platform holding millions of patient records under HIPAA and GDPR, and we onboard new practices every month. Our job is to give patients an experience that feels considered at every step, to make the practice itself run efficiently, and to grow that practice as a result. Data is what makes all three possible. A patient's journey with a practice runs from first lead through consultation and procedure to follow-up, and a practice can only improve that journey, staff against it, and invest behind it when the numbers describing it are right.

Getting that picture back to our clients accurately is a large part of what they pay us for. Every practice that joins us arrives by migrating off a legacy system, so migration here is ongoing work rather than a one-time project. While we are a remote-first company, we are currently only able to hire candidates located in the following U.S. states: CA, CO, FL, GA, IL, MN, OK, OR, PA, RI, TX, UT, and WA.

We hope to expand to additional states in the future.

We are seeking a Senior Data Engineer to be the most experienced engineer on our data team. This is a hands‑on role by design. You will pair with the team daily, set the standards the data function works to, and take the lead on the architecture decisions that shape what we build next. The remit is broad. You will own the target design for the warehouse and the curated layer that sits on top of it, define the semantic layer so that every business metric carries one agreed definition, and establish the standards for naming, lineage, and data quality that the whole function works to.

All of it serves the same end. A practice should be able to see the patient journey clearly enough to improve it, see where its time and capacity actually go, and know which of its decisions grew the business. You will also take migration from something skilled people do carefully to something the platform does repeatably.

Our warehouse is Amazon Redshift running a medallion model, fed from PostgreSQL by Python pipelines and from marketing sources by Fivetran, with Power BI on top. You will own the recommendation on where that architecture goes next, and then you will build it.

We also build with AI as a default rather than as an experiment. Coding agents draft quickly here, so the skill we hire for and level on is knowing what to build and recognizing what is wrong with a draft. Our data team already works with Claude Code connected to the warehouse every day.

Architecture and Modeling
Own the target architecture for the warehouse and the curated layer, decide where that design goes next, and write down the reasoning behind the call.
Design the semantic layer as a single metric repository, where each business metric means one thing and the definition, the formula, and the reasoning behind it are all recorded.
Set the standard for naming, labeling, and lineage, and bring enumerated values and business rule history into data the warehouse can resolve directly.
Build the configuration model for…