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описание
We are growing the maintenance engineering team: the people who take on the hardest client cases and keep the platform healthy. This is not a call centre and not a first line: what reaches this role are the cases that require reading code, writing SQL and proving the root cause with facts. The role has no direct client communication: all communication is internal, with English-speaking colleagues.
задачи
Investigate escalated client tickets, from the symptom in the UI down to the root cause in the code, the database or data.
Write SQL against production PostgreSQL replicas to reconcile platform data, API and MMP trackers.
Read the product’s Python / Django code and run targeted operations via management commands and the Django shell.
Diagnose data pipelines (Airflow) and services running in Kubernetes; work with logs and metrics in Grafana / VictoriaLogs.
Grow the platform’s monitoring: build dashboards and alerts for whatever breaks most often, so that problems are found before clients report them.
Deliver conclusions to the product and engineering teams: a proven root cause, a reproducible bug report, a clear investigation summary.
Turn one-off investigations into runbooks and automations: we are actively building an internal AI toolkit (skills for Claude Code) and expect you to develop it further.
требования
2+ years of experience in L2 technical support or a similar technical troubleshooting role (QA, SRE / DevOps, backend).
Confident SQL: joins, aggregations, window functions, comfortable working with large production datasets, without putting production at risk.
Experience in troubleshooting applications, APIs and databases using various tools and approaches.
Experience in analyzing application and server logs: ability to parse and read log files.
Linux and Bash scripting basics, confident work in the command line.
Experience with monitoring systems (Grafana, Prometheus / VictoriaMetrics, Datadog, etc.): not only reading ready-made dashboards, but building monitoring for the task at hand: metrics, dashboards, alerting.
Experience with REST APIs: reading the documentation, sending requests, interpreting responses and errors.
Experience with utilizing AI-based tools for work-related tasks: log and data analysis, drafting documentation, automating routine work.
Confident use of Google Sheets / Microsoft Excel.
A systematic approach to investigations: hypothesis → check → proof, rather than “restarted it and it works now”.
Focus on the outcome for the client: the ability to prioritise and run several investigations in parallel.
Good written English (B2 and above): internal communication with English-speaking colleagues, tickets and documentation are in English.
Care when working with production data: understanding the difference between read-only and “let me just fix it in the database”.
Будет плюсом: Python at the level of “I read other people’s code and write scripts for myself”; familiarity with Django is a strong plus; Experience with Apple Ads, mobile performance marketing or MMPs (AppsFlyer, Adjust, Singular); Kubernetes (kubectl) and Airflow at a user level