22 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You split time between data and the website. Morning might mean checking traffic dashboards, running scheduled tests, and reviewing server or database logs (bandwidth, server load, Amazon Redshift queries).
Afternoons often include meetings with designers or product people to align the site with business goals, updating content or prototypes (HTML/JavaScript), and troubleshooting issues reported by users or automated alerts.
Expect analytics and cloud tools like AWS (EC2, Redshift) for hosting and data, plus front-end stacks: HTML, JavaScript, AJAX for interactive pages. You’ll also use Adobe tools (Photoshop, Illustrator, Acrobat, Creative Cloud) for graphics and documentation.
You might run tests, record browser/device types, and optimize images for performance, while logging database performance and server metrics for reports.
The U.S. Bureau of Labor Statistics reports about 113,330 employed and a median wage of $104,000 per year. The lowest tenth earn about $53,750 and the top tenth about $201,550 (BLS).
Pay varies by region, company size, and whether you manage data engineering (Redshift/EC2) or mostly front-end analytics.
Begin with HTML, JavaScript, and SQL to read and run analytics queries. Practice with real sites: make prototypes, wireframes, and simple interaction diagrams, then test them across browsers and devices.
Learn basic AWS (EC2, Redshift) to see hosting and data flow, and use Adobe Photoshop or Illustrator to edit and optimize graphics. Build a small portfolio showing tests, traffic reports, and bug fixes.
Compared with a web designer: you focus more on traffic, performance, and analytics. Designers build visual UI and moodboards; you also check server load, database performance, and user behavior to guide design changes.
Compared with a data analyst: you work closer to the website code and hosting (HTML/JavaScript, AWS). You create prototypes, run site tests, and optimize content, not only run large-scale statistical models.
AI can automate report writing, suggest A/B test ideas, or help generate HTML/JavaScript snippets. Use AI to draft documentation, testing protocols, and SQL queries—but always review outputs for correctness and security.
Never feed production credentials or private user data into public AI. Validate any generated code in a test environment, run site tests, and document changes in your technical docs (testing protocols, sequence diagrams).
Become very good at measuring and interpreting website performance: know how to monitor traffic, server load, bandwidth, and database response times and tie them to user behavior.
That means writing clear tests, reading logs (EC2, Redshift), optimizing images and JavaScript, and then turning findings into concrete fixes or design changes you can implement or hand to a designer.