Senior Data Architect: AI-Driven Warehouse & Metrics

Adoreal Inc.; Salt Lake City (UT); Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Healthcare coverage for you and yourf

401k Plan

Paid time off (PTO) and holidays

Company equity opportunities

Fully remote work environment

Job summary

Adoreal Inc. is a fast-growing vertical SaaS company focused on the plastic surgery industry. We rely on a modern data platform with Amazon Redshift, PostgreSQL pipelines, and Fivetran to deliver patient journeys and practice insights.

As Senior Data Engineer, you will own the warehouse architecture, define the semantic layer, and drive migrations with AI-enabled automation, collaborating with data engineers and product teams in a fully remote role.

Qualifications

• 8+ years in data engineering with end-to-end ownership of a warehouse or lakehouse architecture.
• Deep SQL and Python, with production experience on a modern cloud warehouse (Redshift preferred).
• Hands-on experience building a semantic or metrics layer 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.

Skills

Data engineering

SQL

Python

AI tooling

Semantic layer

AWS

dbt

Airflow

Terraform

Fivetran

Power BI

Redshift

Tools

Power BI

dbt

Airflow

Terraform

Fivetran

AWS

Redshift

Job description

Adoreal Inc. is a fast-growing vertical SaaS company focused on the plastic surgery industry. We rely on a modern data platform with Amazon Redshift, PostgreSQL pipelines, and Fivetran to deliver patient journeys and practice insights.

As Senior Data Engineer, you will own the warehouse architecture, define the semantic layer, and drive migrations with AI-enabled automation, collaborating with data engineers and product teams in a fully remote role.