Smarsh Sites

Smarsh Sites

Sr. Technical Product Manager

US - Remote · Senior

Sponsorship not specified$160k-$180kDetected 11 days ago
DynamoDBAWSSite Reliability EngineeringMachine LearningProduct ManagementProduct StrategyCommunicationCollaboration

About the role

  • You will blend deep technical expertise with a strong sense of product ownership to define the future of how our company handles data at scale, directly influencing the productivity and innovation of hundreds of engineers.

Responsibilities

  • Translate high level business objectives into actionable technical roadmaps, anticipate risks and develop mitigation strategies, and closely work with Iteration Manager to align delivery cadences.
  • Manage the Asset Lifecycle: Oversee the entire lifecycle of our workload assets-from evaluating new technologies and market trends to managing versions and planning the graceful deprecation of legacy systems.
  • AI Transformation: Build the business case for ongoing investment in AI, monitoring unit economics, leading cross-functional collaboration with AI/ML engineers and delivering AI solutions.
  • Lead Through Influence: Collaborate closely with key stakeholders in security, compliance, finance, and application development to ensure our workload assets are secure, cost-effective, and aligned with business goals.
  • A deep understanding of the competitive landscape, both internal (existing solutions) and external (cloud provider offerings, open-source technologies), to make build-vs-buy decisions.
  • You will develop a clear product strategy and roadmap that aligns with the broader goals of the engineering organization.
  • Drive adoption and enablement by working with developers to create clear documentation, best practices, and training materials.
  • You are comfortable leading technical discussions on topics like system design, APIs, and database architecture.

Requirements

  • Ability to think strategically and execute methodically, managing multiple competing priorities with precision and adapting to dynamically changing circumstances.
  • Experience taking AI solutions from concept through scale is essential.
  • A strong technical background with hands-on experience in software engineering, SRE, or infrastructure/cloud architecture.
  • Strong technical acumen with ability to evaluate AI/ML approaches, assess feasibility, discuss trade-offs with data scientists and engineers, and make informed trade-off decisions.
  • Bachelor's degree in computer science, Engineering, Business, or related field, or equivalent professional experience.

Nice to have

  • Executive Communication: Demonstrated experience engaging with and influencing senior leadership.
  • Advanced AWS Expertise: Deep familiarity with AWS services, architectures, and pricing models.
  • AWS certifications (e.g., Solutions Architect) are highly desired.
  • Strategic Execution: Ability to think strategically and execute methodically, managing multiple competing priorities with precision and adapting to dynamically changing circumstances.

Compensation

  • $160k-$180k

Benefits

  • Define and deliver on a multi-year vision for our portfolio of workload assets.
  • Champion the platform's vision and services across the organization.
  • Excellent communication skills, with the ability to articulate a compelling vision and articulate complex technical concepts to both technical and non-technical audiences.

Company info

  • Our mission is to build the definitive engineering platform that accelerates developer innovation.
  • We empower our teams by providing them with intelligent, scalable, and self-service tools, enabling them to build the future without being constrained by infrastructure complexity.
  • Drive the Roadmap: Translate complex technical requirements and user needs into a prioritized backlog of clear user stories, epics, and product specifications. You will work within an agile environment to ensure a steady flow of value to our customers.

This listing is sourced directly from Smarsh Sites's careers page and normalized into a canonical job model.