Senior Data Engineer

Adoreal

15d•Remote

About The Position

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.

Requirements

8+ years in data engineering, including end to end ownership of a warehouse or lakehouse architecture, from ingestion through to the curated layer that people actually query.
Deep SQL and Python, with production experience on a modern cloud warehouse. Redshift is what we run today, and Snowflake, BigQuery, or Databricks experience transfers.
Hands-on experience building a semantic or metrics layer yourself, using dbt or an equivalent tool.
Production experience with orchestration and infrastructure as code on AWS (e.g., Airflow and Terraform).
Experience designing and maintaining dimensional or Data Vault models in production.
Data quality testing and observability built into the pipeline, using dbt tests, Great Expectations, or an equivalent, alongside daily use of coding agents and AI tooling in your own work.
AI applied inside a data platform, such as LLM classification of unstructured records, entity matching, anomaly detection, or natural language querying over a semantic layer, with an evaluation set that told you how well it worked.
Experience with regulated data, such as healthcare or financial data.

Responsibilities

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 practice-specific business rules, so that each new practice's setup arrives as data the platform can apply.
Pair with the data engineers daily. Review their work in a way that teaches, and let them review yours.
Build the gold layer and a baseline report library that every new practice gets from day one, covering the patient journey, practice capacity, and growth.
Extend infrastructure as code and CI across the warehouse and its pipelines, with tests that catch schema drift when the product changes.
Bring the unstructured clinical record into the warehouse in structured form, including the journals, notes, and form submissions that arrive as HTML and PDF.
Deepen the automation around migration profiling, delta reconciliation, data quality checks, and report provisioning.
Put AI to work inside the pipelines for enrichment, anomaly detection, and metadata generation, including a data dictionary and enum catalog that stay current because they are generated.
Make the warehouse safe and useful for AI. That means a complete catalog with descriptions and enumerations, a semantic layer that natural language queries resolve against instead of raw tables, a golden set of questions that measures accuracy before business stakeholders get access, and strict adherence to the rules governing which tools may touch patient data.
Take migration from something skilled people do carefully to something the platform does repeatably, with pre-migration profiling, a matching framework for post go-live deltas, LLM-assisted schema mapping and record matching where it earns its place, and rejected record reporting so that nothing is dropped silently. The design target is two practice go-lives a month.
Partner with the engineering teams on the platform changes the data function depends on, and define with them the change contract that keeps the platform and the warehouse in step as new fields and statuses ship.
Work directly with product and commercial leadership on what to measure, not only on how to measure it. You should be the person who can tell a stakeholder which number actually says whether the patient experience improved, whether the practice got more efficient, or whether it grew.