You will be Doctronic's first dedicated data engineer, and you will own the plumbing end to end: how data moves from our production systems into our lakehouse and warehouse, how it gets transformed into trusted, documented tables, and who can access what.
This role serves every team in the company: AI engineering, product, finance, partnerships, and data to name a few.
Build reliable, monitored CDC pipelines from our production databases (MariaDB, PostgreSQL, MongoDB) into our S3 + Iceberg lake and Snowflake
Stand up a transformation layer (e.g. dbt) on Snowflake so core business metrics (visits, bookings, revenue, retention) come from tested, version-controlled models
Select and implement an orchestration tool so pipelines and dashboard refreshes run automatically, with alerting when they break
Design and enforce the access control model for patient data: row/column-level PHI restrictions, HIPAA Safe Harbor compliance, anonymization pipelines, and account deletion workflows
Establish a single governed copy of production data that analytics, finance, and the AI team all read from
Support the AI team's data needs for model training
Design and build a best-practice warehouse architecture with clean raw, transformed, and business-ready layers powering our executive dashboards
5+ years of data engineering experience, including ownership of production data platforms end to end
Strong SQL and Python, with experience building and operating ELT/CDC pipelines (Fivetran, Airbyte, or similar)
Hands-on experience with a modern lakehouse/warehouse stack: S3, Apache Iceberg, a catalog layer, and Snowflake or an equivalent warehouse
Experience with transformation frameworks (dbt or similar) and orchestration tools (Airflow, Dagster, Glue workflows, or similar)
Solid AWS fundamentals: IAM, Lambda, Kinesis, Glue
A pragmatic, reliability-first mindset
Comfort operating with high autonomy and minimal specs in a flat, engineering-first organization
Strong communication skills; you'll work directly with product, marketing, finance, and AI stakeholders
Experience with HIPAA/PHI data governance, anonymization, or healthcare data
Experience with event/behavioral data pipelines (ClickHouse, GTM/server-side tracking, CDPs)
Familiarity with ML data workflows: feature pipelines, training datasets, notebook environments (SageMaker, Databricks, Jupyter)
Experience with BI tooling (Metabase or similar) and semantic/metrics layers
Prior experience as the first or only data engineer at a startup
Base salary range: $200,000 to $275,000 annually, depending on experience, plus meaningful equity
Parental Leave: 12 weeks fully paid parental leave for all parents, regardless of gender or path to parenthood β no distinction between birthing and non-birthing parent
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