Mid-Level AI Engineer: Data Pipelines & ETL Automation

HealthFirst Medical Group

Naperville (IL)

On-site

USD 97,000 - 138,000

Full time

14 days+

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Benefits offered by this job

Wellness perks
Excel training
Flexible hours

Job summary

HealthFirst Medical Group in Naperville, IL seeks a mid-level AI Engineer to automate processes, enhance data tooling, and shape product decisions. The role offers a salary range of $97,000 to $138,000 and emphasizes Excel-driven ETL pipelines, observability, and continuous learning.

The candidate will work on data pipelines, drive improvements across Pandas-based services, and collaborate with stakeholders to reduce technical debt while delivering reliable, scalable analytics capabilities.

Qualifications

  • 5 years of Plotly experience.
  • Experience with ETL pipelines and data tooling.
  • Strong communication and discretion.

Responsibilities

  • Push Excel changes safely behind flags for Naperville, IL rollbacks.
  • Keep the Pandas build pipeline green so Naperville deploys never wait on a red light.
  • Champion engineering excellence and continuous learning within HealthFirst Medical Group.
  • Map data flow across HealthFirst Medical Group's Pandas services and spot the leaks.
  • Ship the Excel autonomy-rich rewrite that pays down years of HealthFirst Medical Group technical debt.
  • Turn vague technology tickets into crisp, testable ETL pipelines acceptance criteria.
  • Stand up observability so HealthFirst Medical Group sees failures before customers in IL do.
  • Refactor the technology module HealthFirst Medical Group has been afraid to touch.

Skills

LangChain
Facilitation
Pandas
Plotly

Tools

Pandas
Plotly
Excel

Job description

HealthFirst Medical Group in Naperville, IL seeks a mid-level AI Engineer to automate processes, enhance data tooling, and shape product decisions. The role offers a salary range of $97,000 to $138,000 and emphasizes Excel-driven ETL pipelines, observability, and continuous learning.

The candidate will work on data pipelines, drive improvements across Pandas-based services, and collaborate with stakeholders to reduce technical debt while delivering reliable, scalable analytics capabilities.

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