Complete Guide: How to Become a Data Engineer in 2024 (Skills, Salary & Career Path)
Data engineering: the infrastructure of the modern organization
Data engineering is the discipline of building the systems that move, transform, and store data so that it can be analyzed, reported on, and acted upon. Without it, data science has no reliable data to work with. Business intelligence has no pipeline to query. Machine learning has no training sets.
The role has grown significantly in demand over the last decade and shows no sign of slowing. As organizations generate more data and invest more in using it, the need for engineers who can build and maintain data infrastructure grows correspondingly.
What data engineers actually do
The core work of a data engineer is building and maintaining data pipelines: automated systems that extract data from source systems (databases, APIs, files, event streams), transform it into a useful shape, and load it into destination systems (data warehouses, data lakes, databases used for analytics).
The ETL (extract, transform, load) pattern is the foundation. In practice, the work also includes:
Schema design and data modeling. Deciding how data is structured in the warehouse affects how efficiently it can be queried and how flexible it is as requirements change. Star schemas, normalized models, and dimensional modeling are all relevant approaches depending on the use case.
Pipeline reliability. A data pipeline that fails silently or produces incorrect output is worse than no pipeline at all, because incorrect data leads to incorrect decisions. Data engineers build monitoring, alerting, and data quality checks into their pipelines.
Performance optimization. Queries against large datasets need to be efficient. Data engineers design partitioning, indexing, and aggregation strategies to make analytics workloads tractable at scale.
Infrastructure management. Data pipelines run on infrastructure that needs to be provisioned, scaled, and maintained. Familiarity with cloud platforms, Docker, and infrastructure-as-code tools like Terraform is increasingly expected.
The skills to build
SQL. Data engineering is built on SQL. You need to be fluent, not just functional. Window functions, CTEs, aggregations, and query optimization are all necessary skills.
Python. The dominant language for data pipeline development. Libraries like Pandas, SQLAlchemy, and Pydantic are standard tools. Scripting, API integration, and data transformation are all done in Python in most data engineering roles.
A cloud data platform. BigQuery (GCP), Redshift (AWS), and Snowflake are the dominant analytical data warehouses. Knowing at least one well is essential.
An orchestration tool. Apache Airflow is the most widely used pipeline orchestration tool. dbt (data build tool) has become standard for transformation logic within the warehouse. Knowing one or both is expected in most data engineering roles.
Version control and software engineering practices. Data pipelines are software. They should be tested, version-controlled, and reviewed. Data engineers who bring software engineering discipline to their work are significantly more effective than those who do not.
The career path
Entry-level data engineering roles typically require SQL proficiency, basic Python, and familiarity with at least one cloud platform. The most effective way to demonstrate readiness is a portfolio project: an end-to-end pipeline that extracts data from a public API, transforms it, loads it into a database or warehouse, and makes it queryable.
Mid-level and senior data engineers specialize: streaming data with Kafka or Flink, ML infrastructure, data platform architecture, or analytics engineering with dbt. The breadth of the field means there are multiple paths for growth.
The connection to software
Data engineering intersects with software engineering at the API and database layers. Web platforms and applications generate the events and records that data engineers work with. Understanding how the source systems work makes a data engineer more effective.
If you are building software that generates data and want to think about the right data infrastructure from the start, get in touch.