Benchmark Analytics

Product Manager

• Own your product surface end to end — deciding what gets built, when it ships, and how it serves your users or consuming teams, with full accountability for scope, priority, and quality.
• Produce engineering-ready specifications for all features — complete user flows, visual designs, data requirements, edge cases, and acceptance criteria — using AI tools as a core part of your workflow; engineering builds from your specifications directly, without ambiguity about what the experience should be.
• Make priority decisions and defend them — customer success, sales, and leadership will bring you requests; you decide which enter the roadmap and which don’t, with clear reasoning and transparent communication.
• Maintain deep fluency with your customers — whether they are law enforcement practitioners, agency administrators, internal platform consumers, or data science teams — through direct engagement, user research, and ongoing relationship-building.
• Conduct or commission user research as needed — interviews, usability testing, workflow observation, product analytics review — and engage internal subject matter experts to ground product decisions in direct evidence rather than assumptions.
• Partner directly with your engineering pair as a trio; bring clarity on what to build and why while they bring clarity on how to build it and what is feasible.
• Own post-launch adoption and feedback — evangelize new capabilities, train internal teams, track feature adoption, gather user feedback, maintain documentation, and iterate based on real-world usage rather than handing off after launch.
• Support enterprise sales conversations with substantive product and domain knowledge when needed — not just feature lists, but the depth that builds customer confidence.
• Leverage AI tools and technologies across all aspects of your work — research, specification, design, analysis, prototyping, and documentation.

Product Owner

Data Engineer

• Designing, developing, and maintaining scalable data pipelines and ETL/ELT processes
• Contributing to the design and implementation of a greenfield data platform
• Operating in a hybrid greenfield and brownfield environment
• Leading development discussions and defining engineering patterns and standards
• Assessing and analyzing legacy data processes
• Collaborating with cross-functional teams
• Improving data pipeline performance, observability, and reliability
• Supporting documentation efforts for scalability and platform adoption
• Providing technical mentorship and guidance to junior engineers
• Acting as a technical SME in internal and client-facing discussions

Senior Data Engineer

DevOps Engineer

• Design, build, and maintain secure AWS infrastructure across standard and GovCloud environments.
• Own infrastructure-as-code (Terraform / OpenTofu) with a security-first mindset.
• Manage IAM strategy, least-privilege access controls, and cloud security posture.
• Operate and evolve our EKS-based platform including node lifecycle, workload isolation, and cluster security.
• Implement and maintain admission control, network policies, and runtime security tooling.
• Partner with engineering teams on deployment patterns and container security.
• Build and maintain secure, automated deployment pipelines (GitHub Actions).
• Integrate SAST, dependency scanning, secrets detection, and container image scanning into the SDLC.
• Drive shift-left security practices across the engineering organization.
• Maintain and improve security controls aligned to CJIS Security Policy and SOC 2 Trust Services Criteria.
• Effectively and efficiently triage or resolve security alerts by working with engineering teams and/or committing code yourself.
• Manage vulnerability management workflows, prioritization, and remediation tracking.
• Support audit preparation, evidence collection, and control documentation.
• Monitor for threats and respond to security findings across cloud, application, and endpoint layers.
• Enforce and maintain software supply chain security across the organization.
• Serve as an on-call responder for infrastructure and security incidents.
• Drive post-incident reviews and own follow-up remediation items.
• Develop and refine runbooks, alerting, and on-call procedures.
• Identify and implement automation opportunities that reduce manual operational toil.
• Contribute to AI-assisted operations initiatives, including agentic workflows and observability improvements.
• Apply security controls to AI tooling and LLM-integrated systems as they are introduced.

Product Owner

Senior Data Engineer

Senior DevSecOps Engineer