Senior Data Engineer

We need to output HTML fragment with sections: About the role? Actually "Who We Are" is description of company, could be "About the role"? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to include sections only when posting has real specific content. We have "Who We Are" (company description) maybe treat as "About the role"? Could be "About the role" or "About the company". The instruction says use those headings as examples; we can use appropriate headings. We have content: Who We Are (company overview), Who We're Looking For (role description), then bullet list of responsibilities (the "As the Senior Data Engineer, you will:" sections). Then Requirements list. Then Benefits list. Also mention location restrictions (states) maybe as a short paragraph. Also mention remote-first, etc. Also mention no visa sponsorship, work auth, EEO etc - but we should remove boilerplate like EEO, equal-opportunity, scam-warning, legal, navigation, cookie, duplicated lines, application instructions. So we need to strip those. We need to preserve all substantive information: company description, role description, responsibilities (Architecture and Modeling, Building With the Team, Automation and AI in the Data Platform, Migrations and Partnership With Engineering), requirements (list), benefits (list). Also mention location eligibility (states). Also mention remote-first, flexible schedules. Also mention equity, etc. We must not repeat job title as heading. The job title is Senior Data Engineer; we should not put that as heading. Use headings for sections. We need to decide sections: maybe "About the Company", "Responsibilities", "Requirements", "Benefits". Also maybe "Location Eligibility". But location eligibility is not in the list of example headings but we could still use

for it? The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those as examples. Safer to stick to those headings. Location eligibility could be placed in a

within About the Company or Responsibilities? Probably better as a short paragraph under About the Company. We must not create Pay/Schedule/Benefits sections unless posting actually states them. Benefits are stated, so we can have Benefits section. Pay not stated, so omit. Schedule not explicitly stated (flexible schedules mentioned under Benefits? It says "Fully remote work environment with flexible schedules". That's a benefit, could be part of Benefits. So we don't need separate Schedule. We must not create sections with filler like "commensurate with experience". Not needed. We need to use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve all substantive information; do not invent or summarize away detail. Let's extract content. Company description (Who We Are): - fast-growing vertical SaaS company leveraging innovation and disruptive technologies to improve consumer experiences, outcomes, and predictability within the plastic surgery industry. - team thrives on challenges, embraces change, dedicated to transforming how the industry operates. - Data is a critical product within our company, investing heavily in data capabilities. - 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 marketing. - Multi-tenant platform holding millions of patient records under HIPAA and GDPR, onboarding new practices every month. - Job: give patients an experience that feels considered at every step, make practice run efficiently, grow practice as a result. Data makes all three possible. - Patient journey from first lead through consultation and procedure to follow-up; practice can improve journey, staff against it, invest behind it when numbers describing it are right. - Getting that picture back to 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 is ongoing work. - While remote-first, currently only able to hire candidates located in following U.S. states: CA, CO, FL, GA, IL, MN, OK, OR, PA, RI, TX, UT, and WA. Hope to expand. Who We're Looking For (role description): - Seeking a Senior Data Engineer to be most experienced engineer on data team. - Hands-on role by design. - Pair with team daily, set standards data function works to, take lead on architecture decisions shaping what we build next. - Remit broad. - Own target design for warehouse and curated layer on top, define semantic layer so each business metric carries one agreed definition, establish standards for naming, lineage, data quality. - All serves same end: practice should see patient journey clearly enough to improve it, see where time and capacity actually go, know which decisions grew business. - Take migration from something skilled people do carefully to something platform does repeatably. - Warehouse: Amazon Redshift running medallion model, fed from PostgreSQL by Python pipelines and from marketing sources by Fivetran, with Power BI on top. - Own recommendation on where architecture goes next, then build it. - Build with AI as default rather than experiment; coding agents draft quickly, skill we hire for is knowing what to build and recognizing what is wrong with a draft. - Data team works with Claude Code connected to warehouse daily. - As Senior Data Engineer, you will: (then bullet sections) Responsibilities (the bullet sections): - Architecture and Modeling * Own target architecture for warehouse and curated layer, decide where design goes next, write reasoning. * Design semantic layer as single metric repository, each business metric means one thing, definition, formula, reasoning recorded. * Set standard for naming, labeling, lineage, bring enumerated values and business rule history into data warehouse can resolve directly. * Build configuration model for practice-specific business rules, so each new practice's setup arrives as data platform can apply. - Building With the Team * Pair with data engineers daily. * Review their work in a way that teaches, and let them review yours. * Build gold layer and baseline report library that every new practice gets from day one, covering patient journey, practice capacity, growth. * Extend infrastructure as code and CI across warehouse and pipelines, with tests that catch schema drift when product changes. * Bring unstructured clinical record into warehouse in structured form, including journals, notes, form submissions that arrive as HTML and PDF. - Automation and AI in the Data Platform * Deepen automation around migration profiling, delta reconciliation, data quality checks, report provisioning. * Put AI to work inside pipelines for enrichment, anomaly detection, metadata generation, including data dictionary and enum catalog that stay current because generated. * Make warehouse safe and useful for AI: complete catalog with descriptions and enumerations, semantic layer that natural language queries resolve against instead of raw tables, golden set of questions measuring accuracy before stakeholders get access, strict adherence to rules governing which tools may touch patient data. - Migrations and Partnership With Engineering * Take migration from something skilled people do carefully to something platform does repeatably, with pre-migration profiling, matching framework for post go-live deltas, LLM-assisted schema mapping and record matching where it earns its place, rejected record reporting so nothing dropped silently. * Design target: two practice go-lives a month. * Partner with engineering teams on platform changes data function depends on, define change contract that keeps platform and 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. * Should be person who can tell stakeholder which number actually says whether patient experience improved, practice got more efficient, or grew. Requirements: - 8+ years in data engineering, including end to end ownership of a warehouse or lakehouse architecture, from ingestion through to 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. Benefits: - 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 principles - Why Adoreal? (some paragraph about equity, promotions, growth opportunities, core values). This is substantive; we should include as prose maybe under Benefits or separate section? Could be part of Benefits description. We'll keep as prose under Benefits. Also mention: 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. This is substantive but maybe not a separate section; could be a short paragraph under Benefits or after Requirements. It's not boilerplate; it's about visa sponsorship and work auth. We should keep it. Also mention: Adoreal is an equal opportunity employer... etc. That's boilerplate EEO; we should remove. Also mention: *We are not working with search firms for this role, thank you* - maybe minor metadata; could be short paragraph. Recruitment Fraud Notice - boilerplate scam-warning; remove. Thus we need to output HTML with sections: maybe

  About the Company

  (prose),

  Responsibilities

  (ul),

  Requirements

  (ul),

  Benefits

  (ul + prose). Also maybe

  Location Eligibility

  but not in list; we could embed location info in About the Company paragraph. We must not repeat job title as heading. Good. We must not include markdown code fences. Just raw HTML. Let's craft. Structure:

  About the Company

  ... (company description + location eligibility)

  Responsibilities

  • ... each responsibility as a list item? The responsibilities have sub-bullets. We could flatten: each top-level bullet as
  • containing subpoints maybe as
    inside? The instruction: use
    • for any list of duties/requirements/benefits. It doesn't forbid nested lists. We'll do top-level
    • with inner
      for subpoints. Thus:
      • 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