Staff Data Engineer: Scale Data Platform & Pipelines

Bain & Company; Chicago (IL); On-site; USD 141,000 - 169,250; Full time

Benefits offered by this job

401(k) with employer contribution

Comprehensive benefits

Paid time off

Job summary

Bain & Company in Chicago, IL is seeking a Staff Data Engineer I to join Aura, Bain's analytics platform. You will own the data infrastructure powering analytics, ML features, and product experiences, collaborating with Engineering, Product, Data Science, and AI teams.

This role emphasizes architecture, reliability, and scalability, with opportunities to mentor engineers, drive greenfield projects, and optimize data pipelines across the stack.

Qualifications

• 5-8+ years of experience designing scalable data architecture.
• Associate's/Bachelor's degree or equivalent.
• Strong SQL skills and Snowflake experience.
• Experience with Airflow and DBT in cloud environments.
• Familiarity with OpenSearch for log analysis.

Responsibilities

• Own the architecture, reliability, and scalability of Aura's data platform.
• Design, build, and maintain data pipelines from raw ingestion to downstream analytics layers.
• Lead the development of new data infrastructure projects from scratch and own delivery through production.
• Optimize performance and cost across data workflows (e.g., query tuning, storage optimization).

Skills

Advanced SQL

Data architecture

Mentoring engineers

Cross-functional collaboration

Testing/CI-CD

Infra as Code

Leadership

OpenSource tooling

Education

Associate's/Bachelor's degree

Tools

Snowflake

Airflow

DBT

OpenSearch

AWS

Job description

Bain & Company in Chicago, IL is seeking a Staff Data Engineer I to join Aura, Bain's analytics platform. You will own the data infrastructure powering analytics, ML features, and product experiences, collaborating with Engineering, Product, Data Science, and AI teams.

This role emphasizes architecture, reliability, and scalability, with opportunities to mentor engineers, drive greenfield projects, and optimize data pipelines across the stack.