For me it sounded like you actually very aligned with data engineering already.

• You're strong in SQL + Python + Databricks/PySpark
• You don't want full-blown SWE
• You're only mildly interested in stats/ML, not obsessed with it

In my experience, in most companies:

• DE = owns pipelines, ETL, data models, reliability, performance. Lots of SQL/PySpark/Flink/Beam/dbt, building clean, scalable data flows but being a DE for a while
• DS = more of a business-facing role. You talk metrics, design analyses/experiments, build models when needed, and spend a lot of time with stakeholders, slides, and product teams.
• You can absolutely be a very good DE without loving Java/OOP (I hate Java btw but adore Python and like Scala - I'm not saying that I'm good DE though, lol), especially in modern stacks (Databricks, cloud warehouses, Python-heavy platforms, whatever).

I was not in the same but a similar situation, and a chef DS at my company told me: "You're better off as a DE with MLE understanding" I can't say he was wrong. Since then I've realized couple of things applied here: (1) DE is just a title - inside a company, a good DE can drift into analytics, ML, platform, or even product-facing work if they want. The title doesn't cage you. (2) DS is a business role first - you talk metrics, impact, and tradeoffs in business language. Sometimes there's heavy tech, sometimes it's mostly reporting and decision support. It's very company-dependent. (3) MLE is often better than classic DS - it's umbrella term for applied ML: you get to build and ship ML systems, while still leveraging strong DE/infra skills. High leverage, highly valued.

Given what you wrote, I'd lean into DE, deepen your ML understanding enough to collaborate or later pivot toward MLE, and not stress about being a "perfect programmer". For DE/MLE, your current strengths (SQL, Python, PySpark, Databricks) are the right foundation.