Staff Fullstack Software Engineer, Smart Storage Management

Remote - PolandFull-TimeStaffFull-stack

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Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.

Define and deliver the multi-team storage strategy across with measurable outcomes.
Lead architecture for with strong safeguards and observability.
Make high-judgment trade-offs across customer experience, compliance, cost, and operational risk; drive org-wide alignment.
Raise reliability/quality bars (SLO/SLI ownership, validation frameworks, backfill/idempotency, rollout/rollback).
Mentor senior engineers; model high-signal design reviews and effective cross-team influence.
Lead incident posture and prevention; own learning loops and durable fixes.
Partner with PM/Design to set, instrument, and communicate goals.
On-call work may be necessary occasionally to help address bugs, outages, or other operational issues, with the goal of maintaining a stable and high-quality experience for our customers.

12+ years building/operating distributed systems; 3+ years leading multi-team initiatives at Staff scope.
BS degree in Computer Science, a related field, or equivalent experience.
Deep expertise in front-end (JavaScript, React, Angular, HTML/CSS) and back-end (Node.js, Python) development.
Extensive experience with databases such as MySQL, PostgreSQL, or MongoDB.
Expert system/architecture design (pipelines/queues, data modeling, idempotency, backfills, failure recovery, safeguards).
Proven cross-org influence and decision-making optimized for the broader org.
Operational excellence: own SLO/SLIs, lead incidents, prevent recurrence.
High-altitude communication and stakeholder management; crisp narratives when needed.
Models responsible AI tool use (validate edge cases/legacy systems).

Led multi-quarter strategies with measurable cost or Customer Experience outcomes.
Comfortable partnering with eng teams without being code-heavy.
Data platform familiarity (e.g., large-scale validation, Spark/Databricks POCs).