Senior Data Engineer

Doctronic; New York (NY); On-site; USD 200,000 - 275,000; Full time

Job summary

Doctronic seeks a dedicated Data Engineer to own data plumbing from production systems to the lakehouse and warehouse. You will transform data into trusted, documented tables and control access.

This role serves AI, product, finance, partnerships, and data teams across the company. You will build CDC pipelines from MariaDB, PostgreSQL, and MongoDB into S3 + Iceberg and Snowflake, establish a dbt layer for core metrics, and implement an orchestration tool with alerts.

Qualifications

• 5+ years of data engineering experience owning production data platforms end to end.
• Proficient in SQL and Python with ELT/CDC pipelines.
• Experience with lakehouse/warehouse stacks (S3, Iceberg, Snowflake) and catalog layers.
• Familiarity with transformation frameworks (dbt) and orchestration tools (Airflow, Dagster, Glue).
• Strong AWS fundamentals: IAM, Lambda, Kinesis, Glue.
• Ability to operate with high autonomy in a flat, engineering-first org.
• Excellent communication with product, finance, and AI teams.

Responsibilities

• Build robust CDC pipelines from production databases into the lake and Snowflake warehouse.
• Stand up a dbt-based transformation layer for tested, version-controlled models.
• Choose and implement an orchestration tool with automatic runs and alerts.
• Design access control for PHI, HIPAA Safe Harbor, anonymization, and deletion workflows.
• Create a single governed data copy read by analytics, finance, and AI teams.
• Support AI data needs for model training and governance over data products.
• Design and develop a best-practice warehouse architecture with layered data.

Skills

SQL

Python

ELT/CDC pipelines

dbt

Airflow

Dagster

Snowflake

Iceberg

S3

AWS basics

HIPAA awareness

Tools

Fivetran

Airbyte

dbt

Snowflake

Apache Iceberg

S3

Dagster

Airflow

Glue

Job description

The Role

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.

What You'll Do

• 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

What We're Looking For

• 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

Nice to Have

• 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

Compensation & Benefits

• Base salary range: $200,000 to $275,000 annually, depending on experience, plus meaningful equity