20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You’ll start by checking data pipelines and dashboards: sales numbers in Amazon Redshift or query results from Apache Hive, plus any survey responses collected overnight. Expect to spend morning hours cleaning data, running basic statistical tests, and updating visual reports in Canva or Adobe Illustrator for the afternoon meeting.
Afternoons are for meetings: presenting findings to product managers, deciding on product positioning, or briefing the ad team about identified advertising needs. You’ll also sketch new survey designs and write short, clear reports translating numbers into recommended actions.
Learn SQL-based querying first because both Amazon Redshift and Apache Hive use similar query languages; this gets you the raw numbers you’ll analyse. Knowing Redshift helps if the company uses AWS; Hive is common on Hadoop clusters.
After SQL, practice data visualization in Canva or Adobe Illustrator so you can turn tables into presentation-ready charts. Project management basics in Asana are useful too for tracking research tasks and survey schedules.
Use AI to speed tasks: auto-summarize survey responses, draft presentation text, or suggest segmentation based on input features. But never feed raw personal data into public AI services. Keep identifiable customer data inside secure systems like Redshift or your company’s approved environment.
Check model outputs for obvious bias (e.g., one group underrepresented in recommendations). Document your steps: what data you used, what filters you applied, and who reviewed the model’s suggestions before stakeholders act on them.
Salaries vary by location and company size. Entry-level analysts often start around $50,000–$65,000 per year. With 2–5 years’ experience and skills in SQL, statistical techniques, and visualization, expect $65,000–$90,000.
Senior analysts or those in big tech/finance who run large Redshift/Hive clusters and lead research projects can earn $90,000–$120,000 or more. Use Glassdoor or the Bureau of Labor Statistics for local, up-to-date numbers.
Begin with basics: learn question types (multiple choice, Likert scales, open text) and common errors (leading questions, double-barreled questions). Practice by redesigning an existing survey to be shorter and clearer.
Then run small pilots: use a few dozen responses to check if your questions capture the information you need. Learn to code survey data for analysis and visualization, and use Asana to track rounds of testing and interviewer assignments.
A marketing analyst sits between data analyst and market researcher. Like data analysts, you’ll query Redshift/Hive and use statistical techniques. But you focus on marketing questions: product positioning, customer segments, advertising needs.
Compared with market researchers, you’ll do more ongoing tracking and internal analysis (customer satisfaction, employee feedback) and translate findings into presentations and tactical recommendations for product and marketing teams.
Translate numbers into action. That means running the right statistical test, but more importantly creating a clear recommendation: who the target customers are, what messaging to test, and how to measure it.
Concretely, you should be able to take Redshift or Hive query output, make a chart in Canva or Illustrator, and write a one-page summary that tells stakeholders the next three steps and how you will measure success.