If you've ever wondered what data engineers actually do all day — this one's for you.
Not the LinkedIn version. The real version.
I've been in tech for 10+ years and I still get asked "so what do you actually do?" at family dinners. This post is my answer.
9:00 AM — Standup & Morning Triage
First thing I do is not open Slack.
I open my monitoring dashboard.
Most pipeline failures happen overnight while nobody's watching. If something broke at 3am, I want to know before the business analyst in another timezone notices their dashboard is showing yesterday's data.
Once I've checked the alerts I jump into standup. Ours is 15 minutes max. What did I do yesterday, what am I doing today, what's blocking me. That's it.
Then I triage tickets. Some days I have three things to do. Some days I have fifteen. Prioritising is a skill they don't teach you in bootcamp but you learn fast.
9:30 AM — Pipeline Review
This is the unglamorous part of the job that nobody talks about.
Every morning I check whether last night's ETL jobs completed successfully. Did the data land in Snowflake? Are the row counts what we'd expect? Did any data quality checks fail?
If something broke, I investigate. This means reading logs, checking error messages, and tracing back through the pipeline to find where it fell over. Sometimes it's a network timeout. Sometimes it's a schema change upstream that nobody told us about. Sometimes it's my own code from two weeks ago coming back to bite me.
Most pipeline fires happen overnight. Mornings are for damage control before anyone notices.
11:00 AM — Deep Work (Headphones On)
This is my favourite part of the day.
I block this time aggressively. No meetings if I can help it. Headphones on. Do Not Disturb on. This is when actual building happens.
What does building look like?
Writing Python ETL scripts. Reading data from an API, transforming it, loading it into Snowflake. Handling edge cases. Writing tests so future-me doesn't break it accidentally.
Building SQL transformations. Using dbt to model data in the warehouse — turning raw event data into clean, business-friendly tables that analysts can actually use.
Debugging. Always debugging. Something that worked last week mysteriously doesn't work today. This is the job.
12:30 PM — Code Review
Before I eat lunch I try to clear my code review queue.
Code review is one of the most valuable things a senior engineer can do for a team. Reading someone else's PR carefully, leaving a thoughtful comment, catching a bug before it hits production — that's real impact.
I also get my own code reviewed. Fresh eyes catch things you're blind to after staring at the same function for two hours.
2:00 PM — Stakeholder Meetings
Yes, data engineers go to meetings. A lot of them.
The afternoon tends to be meeting-heavy. This might be a sync with the data analytics team to tell them a new dataset is ready. It might be a session with a product manager to understand the business requirements for a new pipeline. It might be a cross-team call about an upcoming data model change that'll affect three different teams.
The best data engineers I've worked with are great communicators. They can explain a technical concept to a non-technical stakeholder without talking down to them. That skill is worth as much as knowing Python.
3:30 PM — Documentation & Mentoring
Nobody loves writing documentation but everyone loves finding it when they need it.
I try to keep a rule: if I build it, I document it. What does it do, where does the data come from, what should you do if it breaks. Future me and my teammates will thank present me.
This slot also tends to be when junior engineers come to me with questions. Debugging sessions, code reviews, architecture questions. I was that junior engineer once. Paying it forward matters.
4:00 PM — Wrap Up
The last hour is for closing out.
I look at my ticket list. What did I actually complete today? What's moving to tomorrow? Are there any overnight jobs I need to leave notes about?
I clear my Slack backlog — not the other way around. Slack is not a real-time obligation. I check it a few times a day, not constantly.
4:30 PM — Learning Time (Protected)
This is the 30 minutes most people skip and then wonder why they feel stuck.
I keep this time for growth. Reading a technical blog post. Watching a conference talk. Experimenting with a new Snowflake feature. Trying out a new Python library.
The data engineering landscape changes fast. If you're not learning you're falling behind.
The Tech Stack
Here's what I actually use every day:
Snowflake — cloud data warehouse, where most of our data lives and gets queried.
Python — ETL scripts, data transformation, automation, anything that needs logic.
GitLab — CI/CD pipelines, version control and deployments. Every pipeline change is tracked, reviewed and deployed through GitLab. (You'll see Apache Airflow in a lot of job listings for this role — GitLab is what we use at my company, but Airflow is worth learning too.)
Git / GitHub — version control for everything. Every change tracked, every deployment reviewed.
dbt — transforms raw data inside Snowflake using SQL. Makes data modelling collaborative and testable.
Slack — where everything actually happens. For better or worse.
The Honest Truth Nobody Tells You
60% of the job is debugging pipelines that worked yesterday. The other 40% is writing the pipelines that will break tomorrow.
Communication skills matter as much as coding skills. You can write the most elegant Python in the world but if you can't explain your data model to a product manager, you'll be invisible.
You'll never stop learning. The tools change every year. That's frustrating and exciting at the same time.
Boring days are good days. A day where nothing breaks, all pipelines run, and you shipped one clean PR is a successful day. Don't let anyone tell you otherwise.
Want to Become a Data Engineer?
Start with SQL. Then learn Python. Then learn one cloud data warehouse — BigQuery has the most generous free tier if you want to experiment without spending money.
The path is clearer than it looks from the outside.
Drop a 🙋 in the comments if you're on your way in — I'd love to know where you're starting from 👇
Follow me on Instagram at https://www.instagram.com/techqueen.codes for visual SQL, Python and Snowflake tips every week 💙
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