What does a data engineer do?

Data engineers build and maintain the infrastructure that makes data accessible and reliable for analysts and data scientists. They design data pipelines, manage warehouses, and ensure data quality at scale.

Data engineer vs. data scientist — which pays more?

Data engineers typically earn slightly more because the role requires both software engineering and data skills. Senior data engineers at large tech companies earn $150K–$200K. Both are strong, in-demand careers.

Is data engineering a good career?

Excellent — data infrastructure is the foundation of every data-driven business. Demand is high and growing. The skill set (cloud, distributed systems, SQL, Python) is highly portable across industries.

Can a data analyst become a data engineer?

Yes — it's one of the most common transitions. The gap is software engineering skills (OOP, APIs, cloud services). Adding Python engineering, dbt, and Airflow skills on top of existing SQL and analytics knowledge is achievable in 6–12 months.

Step-by-Step Guide

How to Become a Data Engineer

CS or STEM degree; cloud data platform certifications accelerate hiring

First job in 6–18 months

Median $130K/yr

+25% job growth

Salary range

$95K – $190K+

Job growth

+25%

Time to first role

6–18 months

Education

CS or STEM degree

Step-by-step roadmap to your first Data Engineer role

Master Python and SQL deeply

2–3 months

Data engineering is 80% Python and SQL. Focus on pandas, PySpark, advanced SQL (window functions, CTEs, query optimisation), and data types. These are tested in every data engineering interview.

Learn data pipeline orchestration

2–3 months

Apache Airflow is the most commonly required orchestration tool. dbt (data build tool) is standard for analytics engineering. Build a simple ETL pipeline that ingests data, transforms it, and loads it to a data warehouse.

Get proficient with a cloud data warehouse

2–3 months

Snowflake, BigQuery, and Amazon Redshift are the three dominant platforms. Snowflake and BigQuery have strong free tiers for practice. Most data engineering job postings require experience with at least one of these.

Learn Spark for large-scale processing

2–3 months

Apache Spark (via PySpark) is the standard for big data processing. Databricks is the dominant managed Spark platform. The Databricks Certified Associate Developer for Apache Spark exam validates this skill directly.

Build an end-to-end data platform project

2–3 months

Ingest public data → store in a cloud warehouse → orchestrate with Airflow → transform with dbt → visualise with Metabase or Superset. Document the architecture. This project is your main interview talking point.

Is Data Engineer actually the right fit for you?

The 60-second Career Fit Quiz scores you against all 46 career paths — including Data Engineer — based on your actual skills and goals.

No account needed · Results in 60 seconds

Core skills for a Data Engineer

8 key skills expected by hiring managers.

PythonSQLData warehousedbtOrchestrationStreamingCloud data platformsData modeling

Top certifications to get hired faster

Google Professional Data Engineer

Issued by Google Cloud

#1 pick

Databricks Certified Associate Developer for Apache Spark

Issued by Databricks

#2 pick

dbt Fundamentals

Issued by dbt Labs

#3 pick

Backgrounds that transition well into Data Engineer

Software engineerData analystBackend developerDatabase administrator

Job titles to target first

Search for these when applying — they're the standard entry-level titles that lead to Data Engineer roles.

Junior Data Engineer

ETL Developer

Analytics Engineer

Frequently asked questions about becoming a Data Engineer

Full Data Engineer career profile Calculate Data Engineer salary jump

Ready to find your next career move?

PathPilot analyzes your resume with AI and surfaces the 10 career paths where your skills translate best — with fit scores and a 30-day action plan for each.

Free to start — no credit card required
Results in under 60 seconds
10 tailored paths with fit scores