What is Data Engineering?

Data Engineering is the practice of designing, building and maintaining the systems that collect, store, transform and serve data reliably at scale. Data engineers build data pipelines (ETL/ELT), model data warehouses and lakehouses, and make clean, trustworthy data available to analysts, data scientists and applications.

Core skills

SQL and Python are foundational; then distributed processing (Apache Spark / PySpark), workflow orchestration (Airflow, Dagster), streaming (Kafka), warehouses (Snowflake, BigQuery, Databricks), and cloud (AWS/GCP/Azure).

What data engineers build

Batch and streaming pipelines, medallion (bronze/silver/gold) layers, data models, data-quality checks, and the infrastructure that powers analytics and machine learning.

What the role actually involves day to day

Data engineering is the practice of building and operating the systems that move data from where it is produced to where it can be analysed, reliably and repeatedly. In practice the work splits into ingestion (getting data out of databases, APIs, files and event streams), transformation (cleaning, joining, conforming and modelling it), storage (choosing formats, partitioning and table design), orchestration (scheduling, dependencies, retries) and operations (monitoring, data quality checks, cost control). The output is not a chart — it is a trustworthy dataset that analysts, data scientists and applications can depend on.

The skills that matter, and how the layers fit together

SQL and one programming language (usually Python) are non-negotiable. On top of that sit distributed processing with Spark, a table format such as Delta, Iceberg or Hudi, an orchestrator, cloud storage and warehousing, and enough software engineering practice — version control, testing, CI/CD — to keep pipelines maintainable. Most teams organise data in layers: a raw landing zone that preserves the source exactly, a cleaned and conformed layer where types and business rules are applied, and a curated layer modelled for consumption. The medallion naming (bronze, silver, gold) is one popular expression of that same idea.

Example (SQL)

-- A tiny slice of a data engineer's job: aggregate raw events into a daily table
SELECT
  event_date,
  COUNT(*)                    AS events,
  COUNT(DISTINCT user_id)     AS active_users
FROM bronze_events
GROUP BY event_date
ORDER BY event_date;

Transforming raw event data into a clean daily metrics table is a typical Data Engineering task.

Run this example in the free online PySpark compiler

Frequently asked questions

What does a data engineer do?

A data engineer designs and maintains the pipelines and storage that collect, transform and serve data — turning raw data into clean, reliable datasets for analytics and ML.

What skills do you need for Data Engineering?

SQL, Python, distributed processing (Spark/PySpark), orchestration (Airflow), streaming (Kafka), data warehouses (Snowflake/BigQuery/Databricks) and cloud platforms.

What is the difference between a data engineer and a data scientist?

Data engineers build and maintain the data infrastructure and pipelines; data scientists use that clean data to build models and generate insights.

How do I practice Data Engineering for free?

Use PySpark.in — run PySpark and SQL in the browser and solve auto-graded Data Engineering challenges, no setup required.

Open the free PySpark compiler · Data Engineering challenges · Data Engineering jobs