Gambill Data Engineer Track · Path 02 — Bridge

Cross over into DE. About five months.

Bridge is for people who already work with data — analysts and software engineers — who want to become data engineers. You skip what you already own and go deep on the rest. Plan on about five months; the more the diagnostic lets you skip, the shorter it gets. A short placement diagnostic routes you to the right variant; both converge on the same lakehouse core and capstone.

Course homeCurriculum overviewPhase 1Phase 2Phase 3Phase 4
Before week 1

Placement diagnostic

~30 minutes. Four short tasks decide which variant you enter, because an analyst and an SWE arrive with mirror-image strengths.

Four tasks

  1. Write a query with a window function and explain what it computes. → tests SQL depth
    Dataset task1_sales.csv — regional sales by rep by month. Rank reps within each region, or build a running monthly revenue total; there's a deliberate tie in East / 2024-01 so RANK vs ROW_NUMBER vs DENSE_RANK actually diverge.
  2. Given a messy CSV and an API, write a small Python script that loads and reconciles them. → tests Python / engineering
    CSV task2_customers.csv  ·  API task2_customers_api.json — reconcile on customer_id. The CSV is messy (duplicate ids, mixed date formats, blank fields, money as $1,234); the JSON endpoint is clean. Expect matched / CSV-only / API-only / value-mismatch records.
  3. Model a simple business process into facts & dimensions and justify the grain. → tests modeling
    Dataset task3_orders.csv — a wide, denormalized order-line file. Design a star schema (one fact + customer / product / date dimensions) and state the grain in one sentence.
  4. Walk through how you would branch, PR, test, and deploy a change. → tests dev workflow
    Learn Git Udemy — The Git & GitHub Bootcamp (Colt Steele)Coursera — Introduction to Git and GitHub (Google)DataCamp — Introduction to GitO'Reilly — Head First Git
Two variants

Your track

Each module below links to its real lesson week in the Launch phase pages — the content is identical, you just take a different route through it. Chips show the module and the Launch lesson week it lives in; the capstone is numbered for Bridge, your final three weeks.

Analyst → DE

Strong SQL & business sense; lighter on programming, distributed compute, and engineering practice.
~20 wks
Duration
Variant A
Route
You haveSQL, aggregation, business framing, BI reporting instincts.
SkipM05 SQL fundamentals (proven in the diagnostic); M06 becomes a quick review.
Close the engineering gap~4 wks5 modules▾
Core data engineering~7 wks14 modules▾
Platform & production~5 wks9 modules▾
Launch~4 wks4 modules▾

SWE → DE

Strong Python, Git, CI/CD; lighter on data modeling, SQL depth, warehousing, and DQ discipline.
~20 wks
Duration
Variant S
Route
You havePython, Git/PRs, testing, CI/CD, packaging.
SkipM00 CLI/Git basics, M02 Python fundamentals, M27 Git deep — already owned.
Close the data gap~4 wks4 modules▾
Model, pipeline & Spark~7 wks13 modules▾
Platform & production~5 wks8 modules▾
Launch~4 wks4 modules▾
Both variants converge on the same spine — the Databricks lakehouse core (M20–M23), data quality (M24–M26), governance (M30), architecture (M32), and the capstone (M36–M37) — and finish on a defensible portfolio project ending in golden records.