---
esco_uri: http://data.europa.eu/esco/skill/48db96bf-3314-45c6-bad8-fdb6e20e5639
esco_label: "data engineering"
relation: essential
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
---

# data engineering — Data Scientist

As a Data Scientist, 'data engineering' isn’t about *becoming* a full-fledged data engineer, but possessing enough skill to reliably access & prepare the data you need – often without being blocked waiting on another team. Daily this means writing SQL queries (complex joins, window functions) to extract data from databases (PostgreSQL, Snowflake, BigQuery are common). You'll likely use Python with libraries like Pandas/Polars for initial cleaning and transformation *before* modeling, but increasingly you’re expected to move beyond that – building simple ETL pipelines using tools like Airflow or Prefect to automate repetitive tasks. Think 'self-service data prep'.

Typical tasks include handling messy data formats (JSON, XML, logs), dealing with API integrations for external datasets, and understanding basic data warehousing concepts (schemas, star/snowflake models). You'll need to be comfortable with version control (Git) for your transformation scripts. A common pitfall is trying to build overly complex pipelines when a well-crafted SQL query or Pandas script would suffice – prioritize simplicity initially. Another is ignoring data quality issues at the source; you *must* understand where your data comes from and its limitations.

What 'good' looks like isn’t production-level, highly scalable infrastructure (that’s for dedicated DE teams). It’s writing clean, documented, testable code that reliably delivers usable data.  It means understanding the trade-offs between different data storage formats (Parquet vs CSV) and choosing appropriately. It also involves proactively identifying potential data pipeline bottlenecks *before* they impact your modeling work. Being able to debug a failed data load is crucial.

Essentially, you're bridging the gap between raw data and model input.  You’re not building the entire highway; you’re ensuring the on-ramp to your analysis isn’t blocked with debris. This competence allows you to iterate faster, explore more hypotheses, and ultimately deliver higher-impact insights because you aren't constantly waiting for someone else to prepare your data.

## Weitere Anreicherung

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