Senior Data Engineer - Remote Cloud Platform Lead

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

Benefits offered by this job

Fully remote in the U.S. (Central Time

Unlimited Paid Time Off

Medical, dental, and vision plans

401(k) retirement plan

Employer-paid disability and life保险

Job summary

Benchmark Analytics is seeking a senior Data Engineer to lead platform-level work from discovery to production, owning critical components of a greenfield data platform built on Python, Kubernetes, and AWS.

You will collaborate with data science, QA, product, and analytics teams to deliver scalable ETL/ELT pipelines and reusable platform capabilities. Remote work within the U.S. is supported with CST overlap.

Qualifications

• Bachelor’s degree in a STEM field or equivalent professional experience.
• 8+ years of professional experience building and operating production data systems.
• Demonstrated independent ownership of major data systems, platform components, architectural decisions, or complex modernization initiatives.
• Advanced Python software-engineering experience, including modular architecture, type annotations, automated testing, packaging, dependency management, API design, and reusable library or framework development.
• Advanced SQL and data-modeling skills across operational and analytical workloads.
• Experience architecting fault-tolerant ETL/ELT systems that support replay, backfills, schema evolution, and failure recovery.
• Strong experience designing and operating cloud-based production architectures, with AWS preferred.
• Production experience with Docker, Kubernetes, and orchestration frameworks such as Airflow or an equivalent.
• Practical experience with CI/CD, infrastructure as code, automated testing, and production observability.
• Demonstrated ownership of a legacy modernization or platform migration effort, including dependency analysis, migration sequencing, validation, cutover, and operational transition.
• Significant experience diagnosing production incidents, complex data failures, and performance bottlenecks and implementing durable corrective actions.
• Ability to evaluate and clearly communicate architectural tradeoffs involving scalability, reliability, maintainability, security, cost, delivery speed, and team capability.
• Ability to independently convert incomplete or ambiguous requirements into pragmatic technical direction and executable delivery plans.
• Demonstrated technical influence through design reviews, engineering standards, mentorship, or shared platform development.

Responsibilities

• Own the technical design, implementation, rollout, and operational support of major components of a greenfield data platform leveraging Python, Kubernetes, and AWS.
• Lead complex platform initiatives from discovery and architecture through incremental delivery and production adoption.
• Independently translating ambiguous business and technical objectives into sound designs, implementation plans, and production-ready systems.
• Design, develop, and maintain scalable, fault-tolerant ETL/ELT pipelines across structured and unstructured data.
• Build reusable platform capabilities for configuration, execution, logging, metrics, error handling, testing, deployment, and operational support.
• Design data workflows for idempotency, replayability, backfills, schema evolution, partial-failure recovery, and safe production rollout.
• Assess legacy data processes and lead incremental modernization strategies that preserve production continuity while reducing operational risk.
• Establish engineering patterns and standards, lead technical design reviews, and challenging unnecessary complexity or weak architectural assumptions.
• Diagnose complex production, performance, and data-quality issues and drive durable corrective actions.
• Improve platform scalability, reliability, observability, maintainability, security, and cost efficiency.
• Collaborate with application engineering, data science, QA, product, analytics, and client-facing teams to deliver clean, reliable, and production-ready data capabilities.
• Evaluate and integrate AI-assisted or agentic workflows where they provide measurable improvements to data processing, engineering productivity, or system interaction.
• Provide technical mentorship through architecture guidance, code reviews, reusable patterns, documentation, and direct engineering feedback.
• Act as a primary technical subject matter expert in internal, cross-functional, and client-facing discussions.

Skills

Python

SQL

AWS

Data pipelines

ETL/ELT

Cloud architecture

Docker

Kubernetes

Airflow

CI/CD

Observability

Mentorship

Education

Bachelor’s degree in a STEM field

Tools

Docker

Kubernetes

Airflow

Git

PostgreSQL

Spark/EMR

AWS services (S3, Lambda, SQS/SNS, IAM)

Job description

Benchmark Analytics is seeking a senior Data Engineer to lead platform-level work from discovery to production, owning critical components of a greenfield data platform built on Python, Kubernetes, and AWS.

You will collaborate with data science, QA, product, and analytics teams to deliver scalable ETL/ELT pipelines and reusable platform capabilities. Remote work within the U.S. is supported with CST overlap.