Data engineer: pipelines, warehouses and how to start
The roleWhat does a data engineer do
A data engineer builds the systems that move data from where it is created to where people can use it. That means ETL or ELT pipelines, a data warehouse such as Snowflake or BigQuery, and tools that run jobs on schedule.
A typical task: an online shop has orders in one database, payments in Stripe and marketing data in another tool. The engineer builds pipelines that load all of it into the warehouse every hour, cleans it with dbt and makes sure the sales dashboard is always correct.
Data engineering is the foundation for analytics, data science and machine learning. Without clean, reliable data, none of those teams can work.
Day to dayData engineering skills and tools
| Skill | What it is used for every day |
|---|---|
| SQL | Transforming and checking data |
| Python | Writing pipelines and automation |
| Airflow, Dagster | Scheduling and monitoring jobs |
| Snowflake, BigQuery, Redshift | Storing data for analytics |
| dbt | Modeling data in the warehouse |
| Spark | Processing large data sets |
Getting startedHow to become a data engineer
A degree in computer science helps, but many junior data engineer roles go to former analysts or backend developers. SQL is the most important skill to start with.
A practical order: SQL, Python, a cloud warehouse, then Airflow and dbt, then Spark. A data engineering course with real pipelines teaches more than theory, and a data engineer course on a cloud platform adds the tools employers ask for. A data engineer certification from AWS, Google Cloud or Databricks adds credibility.
Prepare for data engineer interview questions on SQL, data modeling, pipeline design and handling failures.
Picked by the Hoorya editorial team: from a first beginner course to advanced level.
Data Engineering Essentials: Databricks, PySpark, and SQL
Databricks, PySpark and SQL basics.
The Complete Hands-On Introduction to Apache Airflow 3
A hands-on introduction to Airflow 3.
Ultimate Python, SQL & Spark for Data Engineering Bootcamp
Python, SQL and Spark in one bootcamp.
Data Engineering with Python
Data engineering with Python.
Udemy list prices in US dollars. Udemy runs frequent discounts, so a course may cost less in your region.
No budget? Build a small pipeline that pulls data from a public API every day, stores it in a free Postgres or BigQuery sandbox and shows it in a dashboard.
GrowthCareer path
Many start as analysts or junior engineers maintaining existing pipelines. Mid-level engineers build new pipelines, and senior engineers design the data platform.
Later steps are lead engineer, data architect or head of data.
Data engineer jobs are common in tech, finance, retail and healthcare. Remote data engineer jobs are widespread.
What you buildWhat this work teaches you
You will learn to build things that run without you: reliable, tested and monitored.
The job also builds care for data quality, because one wrong join can mislead the whole company.
Pros
- Strong demand
- Good pay
- Clear skills to learn
- Many remote roles
- Foundation for AI and analytics careers
Cons
- On-call when pipelines fail
- Less visible work
- Many tools to keep up with
- Pressure when dashboards break
- Legacy systems to maintain
This career often suits the ISTJ type
Take the free Hoorya personality test and see how close your profile is to this specialist’s.
PayData engineer salary in the US and abroad
Plan for learning time and a job search, or a move from an analyst role. The BLS counts data engineers among software developers: entry pay is around $79,850 to $103,050 a year and the median is $133,080. The data engineer salary for senior specialists reaches $169,000 and more.
UnfilteredWhat data engineering looks like day to day
A typical day: checking pipeline alerts, fixing a failed load, building a new dbt model and helping an analyst understand a table.
Bigger projects include moving to a new warehouse or building a real-time pipeline.
Career growth
From junior to data architect or head of data.
Job satisfaction
High for people who like building reliable systems.
Stability
Every data-driven company needs this role.
Work-life balance: fine, 7 out of 10
Mostly regular hours, with occasional on-call when a pipeline breaks.
Remote work is common. Stress is moderate.
