Data science, which I think makes this harder for me because a) things move so fast that you’re out of date within months and b) there is so much surface area of knowledge (math, stats, programming, visualization, ...) that impostor syndrome is an inevitability.

Well in addition to all that Data Science stuff I have become quite knowledgeable about several related fields. At least these topics come to mind:

- Data Engineering: various DB technologies' pros and cons (AWS Athena, Snowflake, MS Sql Server, Elasticsearch, Redis), Airflow, AWS S3

- ML Engineering: AWS autoscaling clusters, EC2, Spark cluster set-up & management, IO/CPU bottle neck identification, optimizing workloads (like how large chunks, how many Spark worker processes, how many threads on Python's libraries, ...)

- Umm Web Engineering?: HTTP API design, load balancing, Docker & Kubernetes, partial results caching for real-time responses

I have understood that on larger organizations each of these could have a dedicated team behind it. Anyway I've been lucky to find this company & role and gotten an opportunitu to learn and apply so many technologies, but I must admit it is getting a bit tricky to keep (shall I say pickle) all that in my brain :D