Role Overview
The Outcome Lifecycle Lead owns and evolves Inventeer’s end-to-end workflow that turns ideas into execution-ready work—combining product framing, engineering consolidation, and delivery planning, with AI as a core accelerator and human validation as a hard gate. This role exists to create speed with clarity: reduce ambiguity before engineering starts, improve predictability across parallel initiatives, and ensure our outputs become reusable assets (templates, standards, playbooks), not one-off efforts. You will work closely with engineering leadership and Product Engineers to improve the system, run the process, and continuously raise the quality bar.
Key Responsibilities
Workflow Ownership (End-to-End)
- Own and evolve the workflow that converts requests into clear, validated inputs for engineering and structured execution plans.
- Define and enforce quality gates (what “good” looks like before work progresses).
- Maintain a durable knowledge system: documentation standards, decision logs, templates, and reusable artifacts.
Product Framing & Intake Quality
- Ensure incoming requests are clarified into outcome-oriented definitions: scope boundaries, goals, user/customer context, success criteria, constraints, and risks.
- Guide stakeholders through structured discovery and ensure the final package is actionable for engineering.
- Identify low-viability work early and raise flags with clear reasoning.
Engineering Consolidation
- Ensure the “engineering-ready” package includes critical technical considerations needed to build safely and efficiently (architecture direction, data/integrations, infra/deploy, security/compliance constraints, testing/operations expectations).
- Validate coherence and feasibility before execution starts.
- Standardize how technical decisions are captured so future work is faster and less dependent on context.
Planning & Execution Readiness
- Produce or validate structured plans: deliverables, milestones, sequencing, dependencies, and ownership.
- Integrate work into our execution tools (e.g., Linear) with consistent conventions.
- Maintain visibility and predictability across multiple parallel initiatives.
Delivery System & Standards
- Build and enforce guardrails: Definition of Done, review workflows, quality checklists, and team cadences.
- Coach the team to operate consistently within the system, improving maturity over time.
- Track delivery signals (quality, throughput, predictability) and drive continuous improvements.
AI-Assisted Workflow Standards
- Define how AI tools are applied in practice: prompt patterns, validation rules, documentation expectations, and a “never accept blindly” discipline.
- Create operational guardrails so AI-assisted output is reliable, auditable, and maintainable.
- Continuously refine AI-assisted workflows based on observed failure modes and team feedback.
Success Metrics (first 30–60 days)
- Requests reach engineering with significantly less ambiguity and fewer back-and-forth cycles.
- Projects start with clear structure (deliverables, ownership, sequencing) and improved predictability.
- Quality standards become consistent across engineers (DoD, reviews, documentation, release habits).
- The workflow is actively used in day-to-day execution (not just documented) and produces reusable artifacts weekly.
- Delivery health signals (cycle time, rework causes, blocked work patterns) are visible and improving.
Required Skills
- High-Intermediate level in English (professional working proficiency) required.
- Strong experience owning cross-functional workflows bridging product thinking and engineering execution (Product Ops, Eng Ops, Delivery Ops, or similar).
- Strong systems thinking: designs processes that scale and resist chaos.
- Solid engineering fluency (architecture/infra/security concepts) even if not a daily coder.
- Excellent written communication: specs, structured notes, decision logs, and standards documentation.
- High accountability and comfort operating with ambiguity early, tightening it through structure.
Nice to Have
- Fluency in Portuguese (written and spoken)
- Experience operating AI-assisted workflows with guardrails and human-in-the-loop validation.
- Background in consulting environments with multiple client initiatives running in parallel.
- Familiarity with KPI/metrics-driven delivery management (cycle time, throughput, WIP, rework).
- Experience in remote-first teams and async documentation culture.
Behavioral Expectations
- You move fast, but you don’t pass ambiguity forward.
- You default to clarity, documentation, and measurable quality.
- You treat AI as a multiplier—never as the final authority.
- You raise team output through standards, coaching, and consistency—not heroics.
- You surface risks early and avoid status theatre.