Job description

Who We Are 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. Who We're Looking For 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. As the Senior Data Engineer, you will: 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 practice-specific business rules, so that each new practice's setup arrives as data the platform can apply. Building With the Team • 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. Automation and AI in the Data Platform • 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. Migrations and Partnership With Engineering • 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. • 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. What We Offer At Adoreal, we believe in supporting our team’s well-being and growth through comprehensive benefits and a collaborative, people-first culture. As a globally remote company, we prioritize flexibility, inclusivity, and teamwork rooted in the Adoreal principles. Benefits & Perks: • Healthcare coverage for you and your family • 401k Plan • Paid time off (PTO) and paid holidays • Company equity opportunities • Fully remote work environment with flexible schedules • Collaborative and thriving team culture guided by Adoreal’s core values Why Adoreal? Joining Adoreal means becoming part of a high-performing, ambitious team that works hard, achieves extraordinary results, and enjoys the rewards that follow. Every employee receives equity, aligning us all in our shared success. Promotions and growth opportunities are limitless, driven only by your performance and dedication to our core principles: • We do what is best for the company, not what is best for individuals of the company—we take pride in our work, but not in pride itself—the success of Adoreal is more important than our egos. • We always share relevant information and admit our mistakes freely and openly (even when it is worrisome or uncomfortable to do so). Candid communication and a solution-oriented mindset is rewarded. • We innovate by finding the right problems, then solving them. We thrive on change that minimizes complexity and improves situations for our stakeholders. • We incentivize efficient results & people over process and focus only on the work that creates the most value for our stakeholders. • We are quietly confident and openly humble, being tenacious in our quest to perform and inspire others with our thirst for excellence. • We do not bow up and knock down—we care about our teammates and treat all of them with respect, regardless of their level within the company. • We are steadfast in our pursuit to better our company, our colleagues, and ourselves. When we hire, we only hire people who bleed our principles and who will hopefully be our replacement one day. At this time, Adoreal is not offering visa sponsorship for this position. Candidates must have current and valid authorization to work in the country where they are applying. We comply with all applicable employment and immigration laws, including U.S. regulations, and encourage applicants to verify their work authorization status before applying.   Adoreal is an equal opportunity employer committed to fostering a diverse, inclusive, and fair workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, marital status, veteran status, or any other protected characteristic under applicable U.S. law (EEOC) or local employment regulations where we operate. We encourage applications from candidates of all backgrounds. Accommodations for disabilities are available upon request throughout the recruitment process. *We are not working with search firms for this role, thank you* Recruitment Fraud Notice Adoreal has become aware of fraudulent job offers being sent by people falsely claiming to represent us. To protect yourself: • Verify the sender's email address matches our official domain before responding. • Be cautious of offers that come with no prior application or interview process. • Never share personal details, banking information, or Social Security numbers as part of a job offer or interview. • Legitimate offers will never ask you to pay money upfront for equipment, training, or onboarding. • If something feels off — urgency, poor grammar, unusual payment requests — trust your instincts and pause before responding. If you receive a suspicious email or offer claiming to be from Adoreal, please don't respond to it directly. Report it as spam through your email provider and reach out to us at Hello@adoreal.com with any concerns.

Requirements

Must-haves

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 practice-specific business rules, so that each new practice's setup arrives as data the platform can apply.
Building With the Team
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.
Automation and AI in the Data Platform
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.
Migrations and Partnership With Engineering
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.
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.
Experience with regulated data, such as healthcare or financial data.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gopythonaillmsqlaws

Platform: workable
Effort: medium

Benefits

Benefits detected in the listing.

Vision

401k

Pto

Paid Time Off