Analyst in 2015
You worked in Excel, used a BI tool and perhaps wrote some SQL that you learned on www.w3schools.com and saved the scripts locally on your desktop
You worked on clearly defined OLAP cubes that were made by the BI team and if you wanted to add new columns you’d have to wait for weeks for the BI team to add them
You spent a lot of time updating spreadsheets and PDF reports for the weekly KPI review meeting. This was tedious but also helped you develop an intuition for the numbers (View Highlight)
Note: Slow & tedious but well-defined & familiarity
Analyst in 2021
Your company uses a modern data stack and you’re pulled into everything from writing tests in dbt to debugging Airflow pipelines. At the same time you’re expected to stay close to business problems
Your company uses ELT instead of ETL and all the data you’d ever dream of is easily available. But with so much data you’re struggling to stay on top of the pipelines and data quality has become a real issue. Metrics are defined all over the place and you’re drowning in Slack alerts from dbt and Airflow and don’t know which ones to pay attention to
People keep asking you to read an article about a new concept called the Data Mesh but you don’t really get what it’s about (View Highlight)
Note: How do you wrangle the complexity of pipelines and still understand the numbers?
