Target profile

You will love this job if:

You are pioneering and innovative and want to be part of the cutting-edge and disruptive crypto-currency world
You are eager to learn new knowledge in both financial and technical fields
You thrive in a non-hierarchical organization with a casual working environment
You enjoy solving complex distributed systems challenges and optimizing streaming data pipelines
You value comprehensive documentation and collaborative problem-solving

Team / Role

As an Engineer you will:

SRE & Observability

Own the observability of the platform end to end: decide what to measure, instrument it, collect it, store it, and put it in front of people
Build Grafana dashboards and alerts that name what broke and what to check next — not alerts that merely report a number crossing a line
Run the production systems: diagnose silent failures, recover missed data, and fix the cause so it doesn’t recur
Ship changes through CI/CD and GitOps, and know the difference between “merged” and “live”
Operate streaming workloads on Kubernetes (EKS) — resource budgeting, config delivery, capacity, and why an OOM-kill can also be a data-loss event
Provision the AWS resources behind the platform as code (we use Terraform; we’ll show you ours)
Participate in on-call for the data and observability platform

Data Pipeline Engineering

Build and maintain the near-realtime pipelines that carry telemetry and logs from source to quarriable table
Reason about delivery guarantees (at-most-once vs at-least-once) and what they mean for the numbers on a dashboard
Design data quality checks — route bad records for investigation rather than dropping them silently
Design and optimize OLAP tables so billion-row queries return fast: sort keys, partitioning, deduplication, retention
Write and tune the SQL behind dashboards and analytics
Translate a request like “show me p99 latency per venue” into a pipeline change and a table design

Required Skillset

University degree in Computer Science, Software Engineering or related disciplines
At least 4-7 years relevant working experience
Observability and monitoring in production — instrumenting services, building dashboards, writing alerts that are worth acting on, and diagnosing a live incident from metrics and logs together (Prometheus, Grafana, Loki, ELK, Datadog, CloudWatch — any equivalent stack)
CI/CD — delivering your own changes through a pipeline (GitLab CI, Jenkins, GitHub Actions, or similar), not handing them to someone else
A distributed stream processing engine in production (Flink, Spark Structured Streaming, or similar)
An OLAP / columnar database — designing tables and tuning queries, not just reading from them (ClickHouse, BigQuery, Druid, Redshift, or similar)
Kubernetes working knowledge — deployments, resource requests and limits, config delivery, reading pod state
Strong programming in at least one of Python, Scala, Java or Rust, plus fluent analytical SQL
Ability to troubleshoot production systems methodically, and the instinct to fix the cause rather than the symptom

It is a plus if:

OpenTelemetry, distributed tracing, or high-cardinality telemetry at scale
Airflow (DAGs, scheduling, task orchestration)
Infrastructure as code beyond the basics (Terraform modules, drift, targeted applies)
Build tools for JVM or multi-module projects (Maven, Gradle, sbt, Cargo)
Familiarity with message queues (SQS, Kafka, or similar) and event-driven architec

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