Staff Software Engineer

SmartLight Analytics•Plano, TX

1d•$140,000 - $170,000

About The Position

SmartLight Analytics is seeking a Staff Software Engineer to drive execution on key initiatives across new products, platform modernization, and re-engineering core workflows. The role partners with senior engineers to turn product priorities into predictable delivery, shape team planning and shipping practices, and build an AI-native delivery system that increases velocity across the software development lifecycle. This is a senior individual-contributor role whose impact is primarily through the team. The engineer delegates ownership, coaches other engineers, and clears obstacles while remaining hands-on where most valuable. The role works directly with the Sr. Director of Engineering on priorities, capacity, and team development, with potential to grow into people leadership as the team expands. Work must meet change-management, documentation, HIPAA, and SOC 2 expectations.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent experience.
At least 8 years of professional software engineering experience, including at least 3 years leading delivery of complex initiatives across a team of engineers as an engineering manager, team lead, or staff-level engineer.
Track record of delivering complex software products through a team, including planning and sequencing work, managing dependencies and risk, and meeting commitments.
Strong hands-on engineering background designing, building, and operating production systems such as web applications, APIs, and data-intensive backend services.
Proficiency with modern AI-assisted development tools and agentic development practices, with sound judgment about appropriate use and guardrails.
Experience building and improving delivery practices, including agile planning, CI/CD, automated testing, and release management.
Experience coaching and developing senior engineers, delegating meaningful ownership while maintaining a high bar for outcomes.
Demonstrated ability to learn a new technology stack and codebase quickly and become effective in it.
Strong problem-solving skills, attention to detail, and accountability for production reliability.
Strong teamwork and communication skills, including the ability to explain status, tradeoffs, and risks to technical and business audiences.
Applicants for employment in the US must have work authorization that does not now or in the future require visa sponsorship for employment authorization in the United States.

Nice To Haves

Prior people management experience, or a strong interest in developing as a people leader.
Experience scaling an engineering team, including hiring, onboarding, and establishing team norms.
Experience rolling out AI-assisted or agentic development practices across a team without compromising quality or compliance.
Experience with modern observability platforms such as Datadog.
Experience with Microsoft Azure, SQL Server, and Snowflake.
Familiarity with healthcare claims data or other regulated data domains.
Experience working in HIPAA and/or SOC 2 regulated environments.

Responsibilities

Own end-to-end execution of major initiatives by translating roadmap priorities into realistic plans, sequencing work, and keeping progress and risks visible to stakeholders.
Break initiatives into well-scoped work and delegate clear ownership across the team.
Help design and build an AI-native delivery system, applying AI-assisted and agentic tooling from planning through release while maintaining quality.
Partner with senior engineers and engineering leadership to turn technical direction into delivery plans and raise risks and tradeoffs early.
Coach and mentor senior engineers on ownership, planning, estimation, and cross-team communication in partnership with the Sr. Director of Engineering.
Shape team structure and capacity plans with the Sr. Director of Engineering and contribute centrally to hiring and onboarding.
Work with Product, Data Engineering, Infrastructure, and QA to clarify scope and priorities, align dependencies and release timing, and set achievable expectations.
Refine delivery practices, including planning cadence, work intake, definition of done, release readiness, and incident follow-up.
Use delivery metrics and observability data to measure predictability and release quality, identify regressions, and remove bottlenecks.
Stay hands-on to unblock critical-path work, contribute to complex changes, review designs and code, and build deep platform familiarity.
Keep delivery, including AI-assisted work, within governance requirements by following change management, maintaining documentation, and incorporating HIPAA and SOC 2 expectations into day-to-day work.