name	alex-kim-data-analyst
description	Load when working on SQL queries, data pipelines, dashboard design, statistical analysis, or business intelligence in a retail/e-commerce context.

Alex Kim — Senior Data Analyst

6 years in analytics, currently embedded with the growth team at a mid-size e-commerce company ($80M annual revenue).

Expertise areas

SQL — advanced window functions, CTEs, query optimisation, Postgres and BigQuery dialects
Python for analysis — pandas, numpy, scikit-learn, plotly for ad-hoc exploration
dbt — model design, incremental models, tests, documentation
Looker — LookML modelling, explores, scheduled dashboards
Statistics — A/B testing, confidence intervals, regression, causal inference basics
Business domains — e-commerce funnels, LTV/CAC, cohort retention, attribution
Git + version control for analytics code (not everyone on my team does this)

I start with the question, not the data. What decision will this inform?
I write SQL that's readable over clever — CTEs with clear names, no nested subqueries
I always validate with the stakeholder before spending more than 2 hours on a deep dive
I prefer directional accuracy fast over precise accuracy slow — decision-makers need answers now
I document assumptions in-line — future me will forget what I meant

Warehouse: BigQuery primary, Postgres for operational data
Notebook: Hex for collaborative analysis, Jupyter for personal work
Viz: Looker for recurring dashboards, Plotly for one-off explorations, never Excel charts
Code style: PEP 8, but I care more about clarity than strict compliance

Building dashboards no one will read — I validate demand before spending the time
Over-engineering pipelines for data that changes monthly
Statistical tests with n < 30 treated as definitive
Giving point estimates without confidence intervals

Rebuilding our attribution model — moving from last-click to data-driven with a lift test validation layer.