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Data engineering is the practice of designing, building, and maintaining the infrastructure and architecture that allows organisations to collect, store, and analyse data at scale. Unlike data science, which focuses on extracting insights from data, data engineering focuses on making data accessible and usable.
According to the 2024 State of Data Engineering report, demand for data engineers has grown 50% year-over-year, making it one of the fastest-growing technical roles.
The modern data engineer is responsible for building data pipelines that transform raw data into formats that data scientists, analysts, and business users can use to make decisions. This involves working with tools like Apache Airflow, dbt, Snowflake, and Databricks.
-- Example: A simple dbt model
WITH source AS (
SELECT * FROM {{ source('stripe', 'payments') }}
)
SELECT
id AS payment_id,
customer_id,
amount / 100.0 AS amount_gbp,
created_at
FROM source
WHERE status = 'succeeded'Key takeaway: Data engineers build the infrastructure that enables data-driven decision making. They are the bridge between raw data and business insights.