Data Platform Engineer I — Scalable Pipelines & ML (Chicago)

Bain & Co.; Chicago (IL); On-site; USD 141,000 - 169,000; Full time

Benefits offered by this job

401(k) plan

Premium medical/dental/vision coverage

Generous paid time off

Fitness reimbursements

Job summary

Bain & Co. is seeking a Staff Data Engineer I for its NGSS team embedded in Aura, Bain’s analytics platform. You will own data architecture, build scalable pipelines, and ensure reliability across production data systems.

Responsibilities include leading greenfield initiatives, optimizing performance and cost, and mentoring engineers. This role emphasizes collaboration across engineering, product, and data science teams in a startup-like data environment.

Qualifications

• 5-8+ years of experience designing scalable data architecture.
• Associate’s or Bachelor’s degree or equivalent experience.
• Strong software engineering, including testing, CI/CD, and code reviews.
• Ability to lead technical initiatives across cross-functional teams.
• Deep SQL expertise and Snowflake experience.

Responsibilities

• Own architecture, reliability, and scalability of Aura’s data platform.
• Design, build, and maintain reliable data pipelines from ingestion to analytics.
• Lead greenfield data infrastructure projects end-to-end.
• Optimize performance and cost across data workflows.
• Establish best practices, testing, documentation, and observability.
• Provide production support and triage data questions from stakeholders.
• Collaborate with Engineering, Product, Data Science, and AI teams.
• Mentor engineers through code reviews and guidance.

Skills

Data architecture

Data pipelines

Python

SQL

OpenSearch

CI/CD

Code reviews

Snowflake

Airflow

AWS

Education

Associate’s/Bachelor’s degree

Tools

Snowflake

Airflow

DBT

Python

Job description

Bain & Co. is seeking a Staff Data Engineer I for its NGSS team embedded in Aura, Bain’s analytics platform. You will own data architecture, build scalable pipelines, and ensure reliability across production data systems.

Responsibilities include leading greenfield initiatives, optimizing performance and cost, and mentoring engineers. This role emphasizes collaboration across engineering, product, and data science teams in a startup-like data environment.