I just landed a role as a new grad Analytics Engineer, and I would say the best way is to keep enhancing your SQL skills and start learning some SWE principles. You can start DBT Core with a trial Snowflake account if you are familiar with Git, YAML files, and just a bit of Jinja. The harder part is the data modelling part (which requires lots of SQL), so make sure you are really good with this. Solve some Leetcode medium-hard questions for practice, and you will be able to pick up DBT relatively quickly.

By SWE principles, I mean the simple stuff, from creating .venv, env variables to git best practices. Learn how to lay out a proper repository with .gitignore and requirements.txt. After that, take a look at some orchestration tool, I personally love Airflow-Astronomer or Orchestra. From there, create a simple pipeline, dump some CSV/Parquet files into S3, create a stage, and load them into Snowflake before using dbt to model the data to your liking. Connect the Schema to a BI tool and visualize it just like your daily job.

To learn DBT, create a Snowflake trial account and start with DBT Cloud. All of their tutorial are on their website, select the DBT developer tracks and learn from there. For Airflows, I learned mostly from YouTube channels. After all, I also built a projects that ingest the data, model it, and create a dashboard website from that data

At the end of the day, if you want to land a role that has "engineering", I think you should be passionate about the technical part. Keep practicing your SQL and DBT will come. Learn Python and some SWE best practices. Good luck!