---
esco_uri: http://data.europa.eu/esco/skill/65e58886-bd1e-4c5b-8ca5-8d9b353c8aa1
esco_label: "data visualisation software"
relation: optional
onet_soc: 15-1252.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# data visualisation software — Software Engineer

For a Software Engineer, 'data visualisation software' isn’t about *creating* polished marketing infographics; it's about rapidly understanding system behaviour and debugging. Daily tasks involve visualising logs (using tools like Kibana, Grafana, or even simple scripting with matplotlib/seaborn in Python), performance metrics from monitoring systems (Prometheus + Grafana is common), and data flows within applications. You’ll use these visuals to identify bottlenecks, anomalies, and regressions *during* development and post-deployment – think spotting a sudden spike in latency on a graph before users report slowdowns. It's less 'art', more 'detective work'.

Typical scenarios include building custom dashboards for internal tools, quickly plotting data from database queries to validate logic, or visualising API response times during load testing.  While dedicated BI tools (Tableau, PowerBI) might be used occasionally for higher-level analysis of user behaviour *if* you're full-stack or working on analytics platforms, the core focus is usually on programmatic visualisation – generating charts and graphs as part of your code or infrastructure.

The pitfalls are often around choosing the *right* visualization. A poorly chosen chart can obscure data instead of clarifying it. Overly complex dashboards become noise.  Also, be wary of automatically trusting visualisations; always verify underlying data integrity. Good visualisation for a Software Engineer is clear, concise, actionable, and directly tied to technical metrics – not just 'pretty'. It’s about turning raw numbers into immediate insights that drive code improvements.

What ‘good’ looks like is being able to quickly prototype a visualization to answer a specific question (e.g., “Is this new feature causing increased error rates?”), integrate it into monitoring pipelines, and share those insights effectively with the team.  It also means understanding when *not* to visualise – sometimes raw data or simple aggregations are sufficient.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.SE (software-engineering preprints).
