🎯 What You’ll Actually DoArchitect and run high-load, production-grade data pipelines where correctness and latency matter.Design systems that survive schema changes, reprocessing, and partial failures.Own data availability, freshness, and trust - not just pipeline success.Make hard calls: accuracy vs cost, speed vs consistency, rebuild vs patch.Build guardrails so downstream consumers (Analysts, Product, Ops) don’t break.Improve observability: monitoring, alerts, data quality checks, SLAs.Partner closely with backend engineers, data analysts, and Product - no handoffs, shared ownership.Debug incidents, own RCA, and make sure the same class of failure doesn’t return.This is a hands-on IC role with platform-level responsibility.🧠What You Bring5+ years in data or backend engineering on real production systems.Strong experience with columnar analytical databases (ClickHouse, Snowflake, BigQuery, similar).Experience with event-driven / streaming systems (Kafka, pub/sub, CDC, etc.).Strong SQL + at least one general-purpose language (Python, Java, Scala).You think in failure modes, not happy paths.You explain why something works - and when it shouldn’t be used.Bonus: You’ve rebuilt or fixed a data system that failed in production.🔧 How We WorkReliability > elegance. Correct data beats clever data.Ownership > tickets. You run what you build.Trade-offs > dogma. Context matters.Direct > polite. We fix problems, not dance around them.One team, one system. No silos.🔥 What We OfferFully remote.Unlimited vacation + paid sick leave.Quarterly performance bonuses.Medical insurance for you and your partner.Learning budget (courses, conferences, certifications).High trust, high autonomy.Zero bureaucracy. Real engineering problems.👉 Apply if you see data platforms as systems to be engineered - not pipelines to babysit.