Data Engineer III — AI-Driven Health Data Pipelines NYC

Garner Health; New York (NY); Hybrid; USD 166,000 - 205,000; Full time

Benefits offered by this job

Flexible PTO

Medical/Dental/Vision

401(k) with company match

Teladoc Health

Job summary

Garner Health is seeking an exceptional Data Engineer III to join our Engineering team, with a high sense of ownership, focused on a modern data stack and Python/SQL data pipelines in AWS. The role is based in our New York City office, with a requirement to work in the office three days per week (Tuesday, Wednesday and Thursday).

You will build, optimize, and maintain data pipelines powering business insights, define reusable datasets for BI, Marketing, and Data Science, and design a federated

Qualifications

• 2+ years of software/data engineering experience in distributed data processing.
• Expertise in SQL and Python.
• Experience building scalable data pipelines and data modeling.
• Familiarity with Airflow, PostgreSQL, and Snowflake.
• Knowledge of data security and HIPAA compliance.
• Healthcare or insurance domain familiarity.
• Strong ownership and accountability in a mission-driven team.

Responsibilities

• Build, optimize, and maintain data pipelines powering our business.
• Define and build abstracted reusable data sets for BI, Marketing, and Data Science Research.
• Design, build, and evangelize a federated data validation framework to monitor data quality.
• Protect our users' privacy and security through best practices.

Skills

SQL

Python

Data pipelines

Airflow

PostgreSQL

Snowflake

Data modeling

HIPAA

Healthcare

Tools

Airflow

DBT

Job description

Garner Health is seeking an exceptional Data Engineer III to join our Engineering team, with a high sense of ownership, focused on a modern data stack and Python/SQL data pipelines in AWS. The role is based in our New York City office, with a requirement to work in the office three days per week (Tuesday, Wednesday and Thursday).

You will build, optimize, and maintain data pipelines powering business insights, define reusable datasets for BI, Marketing, and Data Science, and design a federated