Data analyst career path

Analytics engineering turns the SQL you write for reports into tested, documented tables that everyone else queries. It's usually built in dbt, a tool for managing SQL like code. Pick it if you'd rather fix the data than present it.

Usually when

1-3 years in, especially if you already write the SQL models behind your dashboards

Skills to add

• dbt (models, tests, documentation)
• Git and pull request workflow
• Dimensional modeling
• Snowflake or BigQuery
• dbt Analytics Engineering Certification

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Transformations you built and maintained that other analysts or dashboards depended on, ideally with tests.

Same SQL and metric work, pointed at how people use the product. Funnels, retention and experiments put you much closer to the calls product teams make every week.

Usually when

Any time after a year or two

Skills to add

• Amplitude or Mixpanel
• Event tracking plans
• Funnel and cohort retention analysis
• A/B test readouts

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An analysis of user behavior that led to a product change, and what the change was.

You already write the SQL, define the metrics and explain the results. Data science puts statistics and modeling on top of that.

Usually when

2-4 years in, once you're reading out experiment results or building forecasts

Skills to add

• Python with pandas and scikit-learn
• Hypothesis testing and experiment design
• Regression and classification models
• Causal inference basics

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An analysis that went past totals and averages, like an experiment readout or a forecast, and the decision it informed.

Analytics managers decide which questions get answered and make sure a metric means the same thing in every meeting. If you already push back on low-value requests, you're halfway there.

Usually when

4-6 years in, usually after mentoring other analysts and owning metric definitions

Skills to add

• Prioritizing a request queue against strategic work
• A semantic layer such as LookML or the dbt Semantic Layer
• Hiring and coaching analysts
• Presenting to executives

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Metric definitions or self-serve reporting you led, and the analysts you trained or reviewed.

Strategy and ops teams do the analysis, then own the recommendation and see it through, often on pricing or planning. Presenting to leadership is the harder half, and you may already do it.

Usually when

3-5 years in, if the business decision interests you more than the data behind it

Skills to add

• Financial modeling in Excel or Google Sheets
• Unit economics and pricing analysis
• Annual planning and OKR processes
• Hypothesis-driven problem solving (issue trees)

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Recommendations you made to leadership that were acted on, with the business result.