Senior Data Engineer

Benchmark Solutions LLC•Chicago, IL

4d•$135,000 - $160,000•Remote

About The Position

Dedicated to making a difference in law enforcement agencies across the U.S., our mission is to transform policing by elevating officer performance with a preventative-based early intervention system. Driven by data science and powered by machine learning, our offering analyzes officer performance data in order to identify potentially problematic behavior. In partnership with the University of Chicago, we’ve developed the world’s largest multi-jurisdictional officer performance database, and the only research-driven, evidence-based early intervention system available in policing today. We’re also the only provider of a fully integrated, cloud-based Software-as-a-Service (SaaS) platform that simplifies essential policing workflows. This platform is designed to be a single-source solution for all operational needs, driving extensive efficiency gains and providing best-in-class advanced analytics and insights. Benchmark Analytics provides a comprehensive, all-in-one solution that is advancing police force management through state-of-the-art technology and market-leading data and analytics. This is a senior individual contributor role for a deeply experienced Data Engineer who can independently lead complex platform-level work from discovery through production operation. The successful candidate will operate with limited day-to-day technical oversight, translate ambiguous objectives into executable technical plans, own consequential architecture and design decisions, and build reusable capabilities that increase the effectiveness of the broader data engineering team. This role will be a critical technical contributor to Benchmark’s transformation from legacy ETL systems to a modern, cloud-native architecture built on Python, Kubernetes, and AWS, and AI-assisted and agentic workflows.

Requirements

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

Nice To Haves

Expert-level Python engineering for maintainable production systems, not only standalone scripts or notebooks
Expert-level SQL and strong relational and analytical data-modeling knowledge
Experience handling large data volumes, schema evolution, data-quality enforcement, and complex transformation workflows
Experience with AWS services such as S3, Lambda, SQS/SNS, IAM, and managed data-processing services
Strong experience with Git, Docker, Kubernetes, and automated software-delivery practices
Experience designing reusable platform abstractions and determining when functionality belongs in a shared component versus an individual pipeline
Strong troubleshooting skills across application code, infrastructure, orchestration, databases, and data behavior
Ability to lead through technical credibility and influence without relying on formal authority
Relevant technologies include SQL, Python, AWS, PostgreSQL, Spark/EMR, Git, Docker, Kubernetes, Airflow, Django, and DynamoDB
Experience owning major components of a greenfield data platform or internal developer platform
Experience replacing a commercial or legacy ETL platform with code-first, cloud-native tooling
Experience developing shared Python libraries, frameworks, templates, or platform abstractions used by other engineers
Hands-on experience with Spark and AWS EMR for distributed data processing
Experience working in regulated or security-sensitive environments, including AWS GovCloud
Experience implementing LLM- or agent-based workflows with structured outputs, tool integration, validation, observability, security boundaries, and appropriate human review
Experience serving as a technical subject matter expert in client-facing or cross-functional architecture discussions

Responsibilities

Owning the technical design, implementation, rollout, and operational support of major components of a greenfield data platform leveraging Python, Kubernetes, and AWS
Leading 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
Designing, developing, and maintaining scalable, fault-tolerant ETL/ELT pipelines across structured and unstructured data
Building reusable platform capabilities for configuration, execution, logging, metrics, error handling, testing, deployment, and operational support
Designing data workflows for idempotency, replayability, backfills, schema evolution, partial-failure recovery, and safe production rollout
Assessing legacy data processes and leading incremental modernization strategies that preserve production continuity while reducing operational risk
Establishing engineering patterns and standards, leading technical design reviews, and challenging unnecessary complexity or weak architectural assumptions
Diagnosing complex production, performance, and data-quality issues and driving durable corrective actions
Improving platform scalability, reliability, observability, maintainability, security, and cost efficiency
Collaborating with application engineering, data science, QA, product, analytics, and client-facing teams to deliver clean, reliable, and production-ready data capabilities
Evaluating and integrating AI-assisted or agentic workflows where they provide measurable improvements to data processing, engineering productivity, or system interaction
Providing technical mentorship through architecture guidance, code reviews, reusable patterns, documentation, and direct engineering feedback
Acting as a primary technical subject matter expert in internal, cross-functional, and client-facing discussions

Benefits

A competitive salary and benefits package.
Unlimited Paid Time Off.
Ability to work in a fully remote environment (must be based in the U.S. and willing to work in Central Time Zone).
Summer Half-Day Fridays.
Freed Up Fridays during Spring, Fall, and Winter months to promote productivity and dedicated heads-down work time.
Medical, dental, and vision plan offerings along with 401(k).
Employer-paid Short-Term Disability, Long-Term Disability, and Life Insurance.
Other Voluntary Benefits include additional Life Insurance, Spouse Life Insurance, and Accident Insurance.
The satisfaction that comes with being part of a solution that has real impact in the world.
A diverse workforce and inclusive environment that embraces unique contributions and experiences.
An empowered culture that encourages creativity and professional growth.