Monitoring / AIOps Engineer (Datadog)

TL;DR

Monitoring / AIOps Engineer (Datadog/Splunk): Maintaining and improving monitoring configurations across observability platforms with an accent on alerting rules, SLO-based dashboards, and AI-powered anomaly detection. Focus on implementing log aggregation pipelines, distributed tracing, and automating monitoring for new applications in cloud environments.

Location: Work from the European Union region and a work permit are required. Providing CET-timezone monitoring engineering coverage as part of the European delivery model.

Company

Global tech company with 1,000+ experts in Poland & Eastern Europe, delivering cloud, data, AI, and software solutions for clients including McLaren, Spotify, Disney, ING, and UPS.

What you will do

• Maintain and improve monitoring configurations across Datadog, Splunk, and AppDynamics.
• Build and tune alerting rules to reduce noise and improve signal quality.
• Create SLO-based dashboards and service health reporting.
• Implement log aggregation, metric collection, and distributed tracing pipelines.
• Support deployment and tuning of AI-powered correlation and anomaly detection agents.
• Collaborate with SRE teams on observability initiatives and automate monitoring onboarding.
• Participate in capacity planning, performance analysis, and documentation of standards.

Requirements

• 3–5 years of experience in monitoring, observability, or AIOps engineering.
• Practical experience using AI-powered assistants (e.g., Claude Code, GitHub Copilot) to improve productivity.
• Hands-on experience with Datadog, Splunk, or similar observability platforms.
• Experience with metrics, logs, and tracing pipelines using Prometheus, Grafana, ELK, or OpenTelemetry.
• Understanding of SLI/SLO/SLA frameworks.
• Proficiency in Python or Bash for automation; cloud-native monitoring in AWS, Azure, or GCP.
• Good English communication skills (at least B2 level).

Nice to have

• Experience applying GenAI in SDLC with structured workflows or tool integrations.
• Familiarity with AIOps platforms, ML-based anomaly detection, or monitoring-as-code (Terraform, Ansible).
• Knowledge of distributed tracing tools like Jaeger, Zipkin, or OpenTelemetry.
• Experience in large-scale, multi-cloud environments.

Culture & Benefits

• Support for tech communities, meetups (Software Talks, Data Tech Talks), Guilds, Labs, and personal development budgets for tech and soft skills.
• Modern open-source stacks; trusted partners of Databricks, dbt, Snowflake, Azure, GCP, AWS.
• Real ownership, continuous growth, and a collaborative community-focused environment.

Hiring process

• CV review – HR call – Technical Interview – Client Interview – Decision.