Analytics engineers focus on the data itself, working on issues like data quality, freshness, and proper arrival time, owning the data pipeline from ingestion to visualization. They possess skills in data modeling, SQL, data warehousing, modern data stack tools like dbt, analytics dashboards, and are familiar with transactional database models. Data engineers focus on the data infrastructure of the company site and systems, working on data pipelines, focusing on tasks such as Python programming, DevOps, bash scripting, Git version control, orchestration tools like Airflow, Dagster, and Prefect. They typically work on backend processes to capture customer data, building custom data pipelines, and ensuring proper deployment of applications. When deciding which role is right for your team, consider the pain points you're trying to solve, such as data quality issues, capturing data on your website, establishing a single source of truth for your data, or building a custom data pipeline. If you need help with data quality, are having trouble collecting customer data, or want to establish a unified data view, an analytics engineer might be the best fit. However, if you're struggling with capturing data on your website, building a custom data pipeline, or ensuring proper deployment of applications, a data engineer could be the better choice. Ultimately, understanding the specific needs of your organization will help you make an informed decision about which role to hire.