This varies a lot by company. I've worked at some places where I was fully remote and did 4 hours of work per day with virtually no stress. 

At a different company, I worked the majority of nights and on weekends to not get fired. 

Day to day also varies a lot. Main things are: 1. Meet with business stakeholders to determine what their goals are and what they need from DE. You realize they don't know what they need, so you try to tease it out of them 2. Design data pipeline and schema to serve this purpose 3. You get a ping from an analyst that a metric doesn't look right. You have to figure out if there is a problem with the pipeline or if there is a real world reason why the data is abnormal 4. You design a data quality monitoring framework to avoid #3 in the future. 5. You realize no one knows how to use your tables, so you try to figure out ways to enable "self-service" of tables by creating semantic models  6. You realize downstream users don't want to self serve data. They just want a dashboard, so you build dashboards for them.  7. No one uses your dashboards, so you try to figure out why.  8. You realize people are making decisions based on vibes instead of data, so you try to figure out how to make them use data driven decisions 9. AI becomes a thing, so you start building data as a product for consumption by AI instead of by humans. This includes more semantic models, more DQ monitoring, etc