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Pipelines & data platforms
Data Engineer
Everyone wants the dashboard; somebody has to build the plumbing.
Typical pay
$95k-$180k
How you get in
SQL and programming depth plus cloud data platforms
Outlook
Very strong; demand consistently outpaces supply
Data engineers build and maintain the pipelines and warehouses that analysts and models depend on. It is software engineering applied to data, and it is consistently in higher demand than data science itself.
A day in the life
- 8:30aCheck overnight pipeline runs and failure alerts
- 9:30aFix a job that broke when an upstream schema changed
- 11:30aBuild a new ingestion pipeline for a source system
- 1:00pLunch
- 2:00pOptimize a warehouse query eating the compute budget
- 4:00pData quality checks and documentation
How to get in
Analyst into data engineering
Duration
1-3 years
Cost
$0-$3,000
Credential
None — shipped pipelines
- Start as a data analyst where SQL is the daily tool
- Learn Python properly, then orchestration tools like Airflow or dbt
- Take ownership of the pipelines feeding your own reports
- Move to a data engineering role internally, which is the most common path
Software engineer into data
Duration
6-18 months
Cost
$0-$3,000
Credential
None
- Work as a backend or platform engineer first
- Learn warehousing, dimensional modeling and the modern data stack
- Add a cloud data certification for the platform your target companies use
- The engineering foundation makes this the fastest transition
Degree plus a project portfolio
Duration
4 years
Cost
$40,000-$180,000
Credential
BS in computer science or information systems
- Complete a technical degree with strong programming coursework
- Build end-to-end pipelines on public data — ingestion through warehouse to dashboard
- Publish the code so it can be verified
- Enter as a junior data engineer or analytics engineer
What people love
- · Higher demand than data science, with less competition
- · Strong pay and remote-friendly
- · Real engineering craft
- · Foundational — every data initiative needs it first
What wears people down
- · On-call for pipeline failures
- · Upstream teams change schemas without telling you
- · Invisible when it works
- · Requires genuine software engineering skill, not just SQL