As a data analyst, I’ve often wondered what skills are necessary to transition into a data engineering (DE) role. With many job postings requiring ETL, data pipelining, and data warehousing experience, it’s clear that DE skills are in high demand. But what about entry-level data analysts who want to make the switch?
I’ve been learning SQL, Excel, BI, and some Python, and while these skills are essential for data analysis, I realized that I need to learn more to be competitive in the DE space. So, what DE skills should an entry-level data analyst have?
Apart from Excel, SQL, BI, and Python, I would recommend learning data pipelining tools like Apache Beam or Apache NiFi, as well as data warehousing technologies like Amazon Redshift or Google BigQuery. Additionally, learning ETL tools like Informatica or Talend can be beneficial. These skills will not only make you a more competitive candidate but also give you a deeper understanding of the data ecosystem.
By building a strong foundation in DE skills, you’ll be well-prepared to take on more complex projects and eventually transition into a DE role. So, don’t be afraid to learn beyond the basics of data analysis – it will pay off in the long run!