---
esco_uri: http://data.europa.eu/esco/skill/7e796b51-49d7-4e73-95af-2e7323763f15
esco_label: "design database in the cloud"
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
---

# design database in the cloud — Data Scientist

For a Data Scientist, 'designing databases in the cloud' isn’t about being a DBA; it's about architecting *data pipelines* that scale with experimentation and production models. Daily tasks involve choosing the right cloud database service (Snowflake, BigQuery, Redshift, Azure Synapse, Databricks Delta Lake) based on data volume, velocity, variety, and cost. You’ll be defining schemas optimized for analytical queries – think star/snowflake schemas or denormalized structures for faster feature engineering – *within* those services.  It means understanding how to ingest streaming data (Kafka, Kinesis), batch loads (S3, Azure Blob Storage), and integrate with existing data lakes. The ESCO definition's emphasis on 'adaptive, elastic, automated' translates directly into building pipelines that automatically scale resources during model training or peak prediction times.

## Weitere Anreicherung

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