Each of these is normally a separate hire, a separate desk, a separate ticket queue. I do them all.
01
Data Analyst
Cleaning, slicing, answering questions from data.
02
Business Analyst
Translating business problems into data questions.
03
BI Analyst
Tableau, Power BI, Looker, dashboards executives trust.
04
Reporting Analyst
Recurring reports, KPIs, weekly and monthly cadence.
05
Data Engineer
Pipelines, ETL, warehouses, source-of-truth.
06
Analytics Engineer
dbt, modeling, clean schemas for downstream use.
07
Data Scientist
Statistical models, hypotheses, experiments.
08
ML / AI Analyst
Prediction, classification, ranking, embeddings.
09
Statistical Analyst
Significance, confidence, sampling done correctly.
10
Quantitative Analyst
Numerical models, simulations, optimization.
11
Forecasting / Planning
Demand, capacity, supply, time series at scale.
12
Web Analytics
GA4, GTM, attribution, funnel tracking.
13
Marketing Analyst
Channel ROI, audience splits, campaign reads.
14
Product Analyst
Activation, retention, feature impact, A/B tests.
15
Customer / CX Analyst
Segmentation, churn, lifetime value, support trends.
16
Pricing Analyst
Elasticity, packaging, discount impact.
17
Operations Analyst
Process measurement, bottlenecks, throughput.
18
Supply Chain Analyst
Inventory, logistics, network optimization.
19
Financial Analyst
Budgets, variance, P&L breakdowns.
20
Risk Analyst
Probability, exposure, scenario thinking.
21
Research Analyst
Market, academic, competitive, technical.
22
Database Analyst
SQL design, performance, schema hygiene.
23
KPI / Metrics Owner
Defining what good looks like, end to end.
24
Data Storyteller
Turning numbers into a narrative an exec can act on.
Most companies need at least 4 to 6 of these. Most never coordinate them well.