Common job titles in e-commerce data teams (2026):
- Data Engineer (pipelines, warehouses)
- Analytics Engineer (dbt, metrics)
- Streaming Data Engineer (Kafka, Flink)
- ML Platform Engineer (feature stores, serving)
- Data Platform Lead (architecture, governance)

Typical e-commerce data team:
- 1 Data Platform Lead
- 2-3 Data Engineers
- 1-2 Analytics Engineers
- 1-2 Data Analysts
- 1 ML Engineer
- 1 Data Scientist

As volume grows, the ratio shifts toward
more data engineers and platform roles.

-- dbt model: orders_daily.sql
-- Daily order metrics for the analytics team

{{
  config(
    materialized='table',
    partition_by={'field': 'order_date', 'data_type': 'date'}
  )
}}

WITH orders AS (
  SELECT
    order_date,
    user_id,
    order_id,
    order_total,
    status
  FROM {{ ref('stg_orders') }}
  WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
)

SELECT
  order_date,
  COUNT(DISTINCT order_id) AS orders,
  COUNT(DISTINCT user_id) AS buyers,
  SUM(order_total) AS revenue,
  SUM(CASE WHEN status = 'returned' THEN 1 ELSE 0 END) AS returns
FROM orders
GROUP BY order_date

# Airflow DAG — daily e-commerce pipeline
from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime, timedelta

default_args = {
    "owner": "data-eng",
    "retries": 2,
    "retry_delay": timedelta(minutes=5),
}

with DAG(
    dag_id="ecommerce_daily",
    start_date=datetime(2026, 1, 1),
    schedule="0 2 * * *",   # 2 AM daily
    catchup=False,
    default_args=default_args,
) as dag:

    ingest_orders = BashOperator(
        task_id="ingest_orders",
        bash_command="python scripts/ingest_orders.py",
    )

    run_dbt = BashOperator(
        task_id="run_dbt",
        bash_command="dbt run --select orders_daily+",
    )

    test_dbt = BashOperator(
        task_id="test_dbt",
        bash_command="dbt test --select orders_daily+",
    )

    ingest_orders >> run_dbt >> test_dbt

-- E-commerce SQL — top products by revenue
WITH product_revenue AS (
  SELECT
    p.product_id,
    p.name,
    SUM(oi.quantity * oi.unit_price) AS revenue,
    COUNT(DISTINCT o.user_id) AS buyers
  FROM order_items oi
  JOIN products p ON p.product_id = oi.product_id
  JOIN orders o   ON o.order_id   = oi.order_id
  WHERE o.created_at >= CURRENT_DATE() - INTERVAL '30 days'
  GROUP BY p.product_id, p.name
)
SELECT
  name,
  revenue,
  buyers,
  RANK() OVER (ORDER BY revenue DESC) AS revenue_rank
FROM product_revenue
ORDER BY revenue DESC
LIMIT 20;

# Python — simple e-commerce data pipeline
import pandas as pd
from sqlalchemy import create_engine

# Connect to source and warehouse
source = create_engine("postgresql://source_db")
warehouse = create_engine("postgresql://warehouse_db")

# Extract orders from the last day
orders = pd.read_sql(
    "SELECT * FROM orders WHERE created_at >= CURRENT_DATE - 1",
    source,
)

# Transform: aggregate daily metrics
daily = (
    orders
    .assign(order_date=pd.to_datetime(orders["created_at"]).dt.date)
    .groupby("order_date")
    .agg(
        orders=("order_id", "nunique"),
        revenue=("order_total", "sum"),
        buyers=("user_id", "nunique"),
    )
    .reset_index()
)

# Load into warehouse
daily.to_sql("daily_order_metrics", warehouse,
             if_exists="append", index=False)

print(f"Loaded {len(daily)} days of metrics")

6-month roadmap — e-commerce data engineer:
Month 1: SQL deep dive (joins, windows, CTEs)
Month 2: Python + Pandas + testing
Month 3: Spark basics + data modelling
Month 4: Cloud platform (AWS/GCP) + storage
Month 5: Airflow / dbt + real project
Month 6: Interview prep + portfolio polish

Portfolio projects that impress e-commerce teams:
1. End-to-end order pipeline
   - ingest → warehouse → dbt → dashboard
2. Real-time inventory stream
   - Kafka producer → consumer → alert
3. Data quality checks
   - dbt tests + Great Expectations
4. Feature store demo
   - reusable features for recommendations

Put everything on GitHub with a clear README.