Senior Data Engineer — Cloud-Native Data Platform Lead

Benchmark Analytics; Chicago (IL); On-site; USD 135,000 - 160,000; Full time

Benefits offered by this job

Unlimited PTO

Fully remote in US (CT TZ)

Medical/Dental/Vision

401(k)

Disability/Life Insurance

Summer Half-Day Fridays

Freed Up Fridays

Diverse workforce

Professional growth culture

Job summary

Benchmark Analytics is seeking a senior Data Engineer to lead platform-level work from discovery through production in our cloud-native data stack. You will own architecture decisions, translate objectives into plans, and build reusable data capabilities using Python, Kubernetes, AWS, and AI-assisted workflows.

You will design scalable ETL/ELT pipelines, guide modernization of legacy ETL, and collaborate across engineering, data science, QA, product, analytics, and client teams to deliver

Qualifications

• Bachelor’s degree in a STEM field or equivalent professional experience.
• 8+ years of professional experience building and operating production data systems.
• Independent ownership of major data systems and modernization initiatives.
• Advanced Python software-engineering experience with modular architecture and testing.

Responsibilities

• Own technical design, implementation, rollout and ops of major data platform components.
• Lead complex platform initiatives from discovery to production adoption.
• Translate ambiguous objectives into sound designs and delivery plans.
• Design and maintain scalable ETL/ELT pipelines across structured and unstructured data.
• Build reusable platform components for config, logging, metrics, testing and deployment.
• Improve platform scalability, observability, security and cost efficiency.

Skills

STEM degree|8+ yrs data eng

Python engineering

SQL/data modeling

Cloud-based architectures

Data pipelines

Education

Bachelor’s degree in STEM

Tools

Docker

Kubernetes

Airflow

AWS

Job description

Benchmark Analytics is seeking a senior Data Engineer to lead platform-level work from discovery through production in our cloud-native data stack. You will own architecture decisions, translate objectives into plans, and build reusable data capabilities using Python, Kubernetes, AWS, and AI-assisted workflows.

You will design scalable ETL/ELT pipelines, guide modernization of legacy ETL, and collaborate across engineering, data science, QA, product, analytics, and client teams to deliver