Data Engineer
TechnologyYou build the pipes that move data from where it is to where people want it. Data scientists get the glory, but without you, they'd be staring at empty Jupyter notebooks. You'll build ETL pipelines, manage data warehouses, and debug jobs that fail because someone upstream changed a column name without telling anyone.
Salary Range
Low
$90k
Median
$140k
High
$200k
10-Year Growth
much faster
US Workers
90K
Education
Bachelor's in CS, Data Science, or related + SQL and Python fluency
Environment
remote
Tools & Technical Skills
- ▸SQL and Python for data pipelines
- ▸Apache Spark, Airflow, and dbt
- ▸Data warehouse platforms (Snowflake, BigQuery, Redshift)
- ▸Streaming systems (Kafka, Kinesis, Flink)
- ▸Data modeling (star schema, data vault)
- ▸Cloud data services (AWS Glue, GCP Dataflow)
- ▸Version control and CI/CD for data pipelines
People & Mindset Skills
- ▸Attention to data quality
- ▸Collaboration with analysts and scientists
- ▸Documentation discipline
- ▸Problem decomposition
- ▸Communication of technical trade-offs
Learn the skills
Courses and certifications to get you job-ready
SQL and Python for data pipelines
Data modeling (star schema, data vault)
Cloud data services (AWS Glue, GCP Dataflow)
Version control and CI/CD for data pipelines
What you'll actually do
- 01Build ETL pipelines that move data from 47 different sources into one warehouse that actually works
- 02Debug pipeline failures at 7 AM because the upstream team changed their schema overnight
- 03Optimize queries on datasets so large they make your laptop cry
- 04Design data models that balance performance, storage cost, and analyst sanity
- 05Monitor data quality and catch issues before someone presents wrong numbers to the CEO
- 06Explain to data scientists why their 'simple' request requires rebuilding half the pipeline
Related Shifts
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