Product Engineer (PM)

Search Atlas•Lisbon, ON

14d•Remote

About The Position

Search Atlas is a SaaS B2B company focused on engineering products and services that help businesses improve their performance on Google. They are a rapidly growing organization with clients from Fortune 500 companies to leading Silicon Valley tech startups. The Product Engineer will act as the product strategist, spec writer, QA owner, and stakeholder interface, with the ability to write code when necessary. This role blurs the lines between product and engineering, ensuring the best product decisions are made with technical reality in mind. The Product Engineer will work in a small, high-leverage pod alongside 2-4 engineers, focusing on maximum throughput through clear specs, fast decisions, effective QA, and technical depth. Daily use of AI-native tooling like Claude Code is expected.

Requirements

3+ years of product management experience shipping B2B SaaS products, ideally in a startup or high-growth environment
Hands-on technical background — you have written production code in a prior role, completed a coding bootcamp, or can demonstrably read and navigate a full-stack codebase
Comfortable reading Python and JavaScript/TypeScript; able to write SQL queries, understand REST API contracts, and reason about frontend state
Proven track record of owning QA: writing test cases, executing regression cycles, filing structured bug reports, and managing release sign-off
Strong written communication — your specs, briefs, and status updates are clear, complete, and require minimal back-and-forth
Product mindset: you challenge weak requirements, propose alternatives, and treat every spec as a hypothesis to validate
Hands-on, production experience with Claude Code or equivalent AI-native development tools — used in a real workflow, not just experimented with
Comfortable with ambiguity and autonomous decision-making in a fast-moving environment
Experience working in Agile/Scrum environments with Git-based workflows (GitLab or GitHub)

Nice To Haves

Prior experience as a software engineer, QA engineer, or technical lead who transitioned into product
Familiarity with SEO, content marketing, or digital marketing agency workflows
Experience with observability tools such as Sentry or the Grafana Stack (Loki, Grafana, Tempo, Mimir)
Familiarity with Django REST Framework and React/Next.js application architecture
Experience with end-to-end testing tools such as Cypress or Playwright
Exposure to analytics and event tracking platforms (e.g., Segment, Amplitude, Mixpanel)
Experience working with or building AI-powered products or agentic systems
Comfort working directly in Figma to review designs, flag implementation gaps, and collaborate on handoff

Responsibilities

Own the roadmap. Define what gets built and why. Prioritize ruthlessly, kill low-leverage work, and protect your engineers’ focus.
Write specs that are hypotheses, not instructions. Define the problem before prescribing the solution. Every feature brief ships with a clear success criterion.
Engage stakeholders. Synthesize signal from the CEO, go-to-market, and customer success into decisions with explicit tradeoffs.
Challenge weak requirements. If a spec doesn’t hold up under scrutiny, say so — and bring a stronger alternative.
Read and navigate the codebase. Python/Django backend, React/Next.js/TypeScript frontend. Enough depth to assess complexity, spot scope risks, and make credible estimates.
Debug independently. Inspect network requests, read stack traces, reproduce edge cases, and narrow down root causes before escalating.
Ship small things. Write or modify features, config changes, or test utilities when it’s faster than context-switching an engineer.
Review code with a product lens. Flag scope creep, call out UX gaps, and confirm implementations match intended behavior.
Use AI-native tooling. Claude Code and Cursor are part of your daily workflow, not a novelty.
Own QA end-to-end. Write test plans, execute regression passes, and sign off on deployments.
Build a lightweight QA framework. Test case libraries, edge case inventories, and reproducible bug reports.
Test beyond the happy path. Adversarial inputs, load edge cases, cross-browser and mobile checks.
Own post-release monitoring. Define what healthy looks like, watch Sentry and Grafana in the days after a deploy, and own the response when things go wrong.
Run sprint ceremonies. Standups, planning, retros — lean and outcome-focused.
Keep your engineers unblocked. Decisions that drag kill throughput. Own the clarity that enables speed.
Surface risks early. Flag cross-pod dependencies and blockers to engineering leads and leadership before they become problems.
Communicate async-first. Status, decisions, and tradeoffs in writing. Minimal meetings.
Talk to customers. Work directly with customers and CS to understand pain points and validate product direction.
Instrument features. Define event tracking and funnel analysis for your surface area and use that data to drive iteration.
Own your KPIs. Define success metrics for your pod’s surface area and hold yourself accountable to them.