Site Reliability Engineer, Data & Observability Platform

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
• 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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