Job description

Who We Are: 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. The Role: 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, AWS, and AI-assisted and agentic workflows. 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 Job Qualifications: Required Skills: • 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 Preferred Skills: • 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 Preferred Qualifications: • 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 What We Offer: • 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. Estimated Annual Salary Range: $135k-$160k; based on role, experience, and location Additional Information: • Benchmark Analytics is an Equal Opportunity Employer. We value diversity of all kinds in our effort to create a stellar workforce of committed and passionate team members. • Unfortunately, we are not able to sponsor employment visas at this time, so we can only accept applications from candidates who are authorized to work in the U.S. • If interested, please submit an application or email your resume to Darren.rooney@benchmarkanalytics.com

Requirements

Must-haves

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

Nice-to-haves

Strong experience designing and operating cloud-based production architectures, with AWS preferred
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonkubernetesawsaiapisqldockerci/cdllm

Platform: paylocity
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off