---
esco_uri: http://data.europa.eu/esco/skill/4d85b881-e490-4b4c-897a-2faa4ef53956
esco_label: "manage ICT data architecture"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# manage ICT data architecture — Data Scientist

As a Data Scientist, 'managing ICT data architecture' isn’t about *building* the entire infrastructure – it's about deeply understanding how data flows within it to effectively extract insights. Daily tasks involve collaborating with data engineers and IT to define where data resides (data lakes, warehouses, cloud storage like S3/Azure Blob), how it's structured (schemas, formats - Parquet, JSON, relational tables), and *how* you’ll access it. You're constantly assessing if existing architectures support your analytical needs – can the system handle the volume, velocity, and variety of data required for a specific model? This means knowing enough about pipelines (Airflow, Kafka) to request changes or workarounds when they impede feature engineering or model training.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
