Data Engineer
You build the pipes that move data from where it's created to where analysts and machine learning models can use it. It's unglamorous infrastructure work that everything else depends on.
What Tuesday looks like
You open your laptop to a Slack message: the marketing dashboard is showing zero revenue for yesterday. You spend the first two hours tracing the issue back through three systems and discover an upstream API quietly changed its date format. You write a fix, backfill the data, and document what happened. After lunch you're back to your actual project — migrating an old batch pipeline from one orchestration tool to another, which involves a lot of YAML and a lot of waiting for jobs to run. A data scientist pings you asking why a table is missing a column; you explain it was deprecated last quarter and point to the announcement she missed. At 3pm there's a meeting about data governance that goes 20 minutes long. You spend the last hour writing dbt models and reviewing a teammate's pull request. You leave at 5:30 feeling productive but aware that tomorrow something else will break.
Career profile
Career shape
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In the landscape
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Salary range
No salary data
10-yr growth
+19%
8/10 exposure
Reward profile
3 quick questions to see how this career fits the way you work.
What school costs — and when it pays off
Bachelor's degree · Four years at a public university. Costs here use the cheaper in-state rate.
The chart shows your annual salary over time alongside the annual loan repayment. The shaded band at the bottom is what goes to the loan each year — when it disappears, your full salary is yours.
School cost fully covered by year 8, with strong earnings well beyond that.
Entry-level salary
$100K
25th percentile — what most people start at
Experienced salary
$165K
75th percentile — after ~10 years in the field
School & training cost
$80K
+ $29K interest over 10 yrs
Loan paid off
Year 14
$910/mo for 10 years
First year of work
After loan's paid (yr 14)
Salary range reflects 25th–75th percentile nationally, growing from entry-level to experienced over 10 working years. School costs are national averages — yours will vary. Loan assumes you borrow the full amount at 6.54% interest, repaid over 10 years. Monthly figures are pre-tax.
The first years
Year 1–2: Junior Data Engineer
You're mostly fixing things other people built. Your tickets are small: add a column to a table, debug why a job failed at 3am, write SQL to answer a question an analyst could've answered themselves. You spend a lot of time reading other people's code and Slack threads from two years ago trying to understand why something was built a certain way. Starting pay is usually $75k–$110k depending on city, and you'll feel slow and confused for most of the first year — that's normal.
Year 2–4: Mid-Level Data Engineer
You now own pipelines instead of just patching them. When something breaks at 2am, you might be the one getting paged. You're writing dbt models, designing schemas, and arguing with product managers about whether a metric should be defined one way or another. You start to realize a huge part of the job is communication and documentation, not code. Pay typically climbs to $110k–$150k, and you're expected to mentor the new junior who just joined.
Year 4–5: The Fork
You've gotten good enough that you can see two clear paths. You can go deep on infrastructure — Kafka, Spark, distributed systems, the heavy backend stuff that pays well but pulls you away from the business. Or you can go toward 'analytics engineering' — closer to analysts and product teams, more SQL and dbt, less hardcore systems work. They're both legitimate but they shape what your next ten years look like, and switching back later is harder than it sounds.
Decision point
Specialize in platform/infrastructure engineering (deep technical, closer to software engineering) or analytics engineering (closer to the business, more modeling and stakeholder work). Both pay well but the day-to-day, the people you work with, and your future job titles diverge significantly.
Year 5–7: Senior Data Engineer
You're the person other engineers ask before making a change. You spend less time writing code and more time in design docs, code reviews, and meetings about why the warehouse bill went up 40% last month. You're also watching AI tools write a lot of the boilerplate you used to write — which is fine, because the hard part was never the typing, it was knowing what to build and why. Pay is usually $150k–$200k+ at this point, more at big tech. The grind is real but quieter: fewer late-night fires, more long-term decisions you'll have to live with for years.
Related paths
Data Scientist
Both work heavily with data, but engineers build the pipelines and infrastructure while scientists analyze and model the data. Many people move between these roles.
Machine Learning Engineer
Data engineers often move into ML engineering after building strong data pipeline skills. The transition usually requires picking up more math and modeling knowledge.
Database Administrator
Both careers center on managing data systems, though data engineers build pipelines for analytics while DBAs keep production databases running.