Staff Infrastructure Engineer - Data Platform

Описание

This staff-level data platform infrastructure role focuses on building internal services and tooling for analytics data workflows at Shopify, a company that moves, monitors, and uses data at massive scale.

Responsibilities

• Design, build, and operate internal platform services and CLIs that teams use to author, schedule, validate, and ship analytics and operational data modeling changes safely
• Improve reliability and operability for data platform systems: SLO-minded monitoring, incident response patterns, safe rollouts, and clear runbooks
• Lead large-scale migrations and deprecations (e.g., storage/table formats, orchestration, query engines) with a phased plan, guardrails, and measurable cutover criteria
• Drive cost and performance outcomes at scale: identify systemic bottlenecks, right-size workloads, and build guardrails that prevent expensive failure modes
• Build developer experience for data producers/consumers: fast feedback loops, golden paths, self-serve workflows, and actionable error messages
• Partner across teams to standardize patterns (metadata, access, testing, promotion pipelines) while preserving escape hatches for legitimate exceptions

Requirements

• You design maintainable systems, operate them responsibly in production, and reason about correctness, reliability, and performance when data and platform changes affect many teams
• Production ownership: ships changes with a plan for rollout/rollback, monitors impact, and follows through after launch
• Systems thinking: clear interfaces/contracts, explicit failure modes, and pragmatic tradeoffs between correctness, freshness, cost, and complexity
• Data-change discipline: comfortable reasoning about backfills, idempotency, schema evolution, and blast radius when many downstream datasets depend on a platform behavior
• Proficiency with at least one major cloud platform (GCP, AWS, or Azure)

Nice to have

• Hands-on experience with BigQuery, Airflow/Composer, Iceberg/Delta (or similar open table formats), data lake/lakehouse operations, or large-scale platform migrations

Hiring process

• Be prepared to interview within the week, with the goal to complete the entire interview loop within 30 days