20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You’ll split time between data work, meetings, and change activities. Morning might be cleaning data in Alteryx or running queries in Amazon Redshift, then a mid-day meeting with operations to review workflow bottlenecks you observed on-site.
Afternoon often means writing a short report or slide deck with recommendations, designing a new record layout, or coaching staff on a new process. Some days you lead a project to redesign a business process; other days you’re doing interviews and observations on the floor.
Start with Alteryx and Amazon Redshift (or basic SQL). Alteryx gives fast hands-on data prep and workflows you’ll use every day. Redshift is a common cloud data warehouse where you’ll run analytics.
Learn basics of AWS (how Redshift and S3 store data) next. Apache Hive, Hadoop, and Kafka matter more for large-scale environments; learn them after you can build end-to-end reports and automation.
Use AI as an assistant, not an oracle. For example, use Alteryx or scripts to automate repetitive cleaning steps, and use ML models only after you validate their accuracy with current operational data.
Document assumptions, keep human review in the loop for recommendations that affect staff or finance, and store model outputs and logs in AWS so you can trace decisions if something goes wrong.
According to the U.S. Bureau of Labor Statistics (BLS), there were about 898,280 people in related roles and the median wage is $101,860 per year. The lowest tenth earned $60,640 and the top tenth earned $171,640 (BLS).
Pay varies by city, industry, and how technical your role is (big data skills like Hive, Hadoop, Kafka, AWS often push pay toward the top tenth).
Pick one project: analyze a dataset from start to finish. Use Alteryx for cleaning, then run queries in Redshift or Hive. Host data in AWS S3 and practice loading it into Redshift. Follow tutorials and replicate a dashboard.
Also practice interviewing and on-site observation: visit a small team, map their workflow, and write a short report with at least three concrete recommendations you could measure after implementation.
Compared with a business analyst, a performance analyst spends more time on measuring operational efficiency, doing on-site observations, recommending layout and records management, and leading process-overhaul projects.
Compared with a data engineer, you write fewer production pipelines. Data engineers build and maintain systems like Hadoop, Kafka, and Redshift; you use those systems to analyze workflows, recommend changes, and coach teams on new procedures.