Hiring a good data engineer in 2025 isn’t about stacking keywords on a resume. It’s about finding someone who can keep your systems running when your pipeline breaks at 2 a.m. and your dashboards start blinking red.
I’ve made hiring mistakes. I’ve also worked with engineers who were absolute rockstars. Here’s what I pay attention to now—and what I’ve learned to ignore.
Python and SQL? Yes, But That’s the Bare Minimum
Everyone says they know Python and SQL. But can they use them well?
I’ve seen SQL queries that looked like a Jackson Pollock painting. I’ve also seen pipelines held together by Python scripts that made onboarding new engineers a nightmare.
Fluency in both matters. So does code clarity. Bonus points if they’ve written tests or maintained a shared data library.
They Should Handle Relational and NoSQL Like a Pro
PostgreSQL, Snowflake, MongoDB, Cassandra—you don’t need them to know every tool, but they should understand the trade-offs. I’ve had candidates freeze when asked why they’d pick one over the other.
You’re not just hiring someone to write queries. You’re hiring someone who will shape how data moves through your system.
Real-Time Data Is a Must
If a candidate has never touched Kafka, Flink, or Spark Streaming, I get worried.
Real-time isn’t just hype anymore. Fraud detection, personalization, event tracking—all of it relies on streaming data. Your engineer doesn’t need to be a streaming guru, but they should at least be comfortable in that world.
Ask them to explain a real-time system they’ve built or maintained. If they can’t give specifics, that’s your answer.
Cloud Experience? Show Me What You’ve Built
“Used AWS” on a resume means nothing. Show me how you structured a pipeline in GCP. Walk me through why you chose Redshift over BigQuery. Explain how you kept costs down.
It’s easy to follow tutorials. It’s harder to design something that’s fast, reliable, and doesn't double your cloud bill.
Automation and Monitoring Should Be Baked In
If they’re still manually kicking off jobs or relying on crons with zero logging, it’s a no.
Good data engineers treat their pipelines like production apps. They use Airflow, Prefect, or Dagster. They monitor failures. They care about lineage. And they build things that don’t fall over on a Friday afternoon.
Communication Is a Core Skill
This one doesn’t get talked about enough. Your hire needs to write docs, collaborate in PRs, and explain issues to non-engineers.
If they ghost during incidents or never ask questions in code review, you’ll feel it fast.
TL;DR: What I Look For
- Python and SQL fluency
- Comfort across relational and NoSQL systems
- Real-time data pipeline experience
- Hands-on cloud architecture knowledge
- Workflow automation and monitoring habits
- Solid communication and teamwork Technical skills matter. But mindset, curiosity, and ownership are what keep teams healthy and systems stable.
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