🎯 What You’ll Actually Do
Design and run high-throughput, production-grade data pipelines.
Own data correctness, latency, and availability end to end.
Make hard trade-offs: accuracy vs speed, cost vs freshness, rebuild vs patch.
Design for change — schema evolution, reprocessing, and new consumers.
Protect BI, Product, and Ops from breaking changes and silent data issues.
Build monitoring, alerts, and data quality checks that catch problems early.
Work side-by-side with Product, BI, and Engineering — no handoffs, shared ownership.
Step into incidents, own RCA, and make sure the same class of failure never repeats.
This is a hands-on senior IC role with real accountability.
🧠 What “Senior” Means Here
You:
Think in systems, not tools.
Reason about failure modes, not happy paths.
Can explain why a solution works — and when it breaks.
Take responsibility for outcomes, not just implementation.
Stay effective with incomplete, evolving requirements.
🧠 What You Bring (Non-Negotiable)
5+ years in data or backend engineering on real production systems.
Strong experience with analytical databases
(ClickHouse, Snowflake, BigQuery, or similar).
Experience with event-driven or streaming systems
(Kafka, CDC, pub/sub).
Solid understanding of:
at-least-once vs exactly-once semantics
schema evolution & backfills
mutation and reprocessing costs
Strong SQL and at least one programming language
(Python, Java, Scala, etc.).
You don’t just ship — you own what happens after.
You’ve seen data systems fail in production — and fixed them.
You think about cost when making architectural decisions.
You understand data contracts and change management.
You can translate technical choices into business impact.
You’re comfortable saying: “I don’t know — here’s how I’ll figure it out.”
Not a dashboard-only BI role.
Not a ticket-driven ETL executor.
Not a pure infrastructure or DevOps position.
Reliability > cleverness.
Ownership > process.
Impact > output.
Direct > polite.
One team, one system.
Fully remote (Europe).
Unlimited vacation + paid sick leave.
Quarterly performance bonuses.
Medical insurance for you and your partner.
Learning budget (courses, conferences, certifications).
High trust, high autonomy.
No bureaucracy. Real data problems.
👉 Apply if you treat data like production software — and feel uncomfortable when numbers can’t be trusted.
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