Data engineering, from zero
In-depth, job-focused training, taught by working data engineers.
The gap is real. We built the bridge.
You've read the job descriptions. Every one wants experience you can't get without a job. If you're coming from support, sales, accounts, mechanical, teaching, or a career break, that gap can feel impossible.
It isn't. It just needs the right route, and someone who has walked it.
Find your bridge
Get your tailored route before you talk to anyone.
Three quick questions about where you're starting from and how much time you have each week. No email, no sales pitch. Just a realistic route.
Where are you coming from?
The gap, measured
Here is exactly what stands between you and the job.
This is the standard route. Answer the three questions above and it becomes yours: your pace, your starting point, your week count.
Your route, span by span
- Stage 01, Foundations. Covers SQL · python. Query real data, then start building the pipeline that moves it.
- Stage 02, Pipelines & Data. Covers APIs · pandas. Build a pipeline that survives bad data, retries, and scale.
- Stage 03, Engineering Practice. Covers Git · Docker. Work like a real engineer: reviewed, reproducible, containerized.
- Stage 04, Cloud & Warehouse. Covers Azure · Snowflake. Run the pipeline in the cloud, and query it in a real warehouse.
- Stage 05, Orchestration & Ops. Covers Airflow · dbt. Schedule, test, and monitor the whole thing like production.
- Stage 06, Scale. Covers Spark · Kafka. Handle data too big for one machine, and data that never stops. Walk in able to defend every decision you made getting here.
Total remaining at this pace: 26 weeks.
- 01SQL · python
- 02APIs · pandas
- 03Git · Docker
- 04Azure · Snowflake
- 05Airflow · dbt
- 06Spark · Kafka
TARGET · data engineer
GAP
26 weeks
remaining at standard pace
What a data engineer actually builds
Follow the data. Click any stage.
This is the pipeline you'll learn to build end to end, and the honest version of what goes wrong at each step.
SQL
Ask precise questions of the data. This is the single most-tested skill in a data engineering interview.
What breaks here
One missing index turns a two-second query into a twenty-minute one, and the morning report misses its deadline.
-- the query this stage is testing
SELECT user_id,
RANK() OVER (ORDER BY spend DESC) rank
FROM orders_summary;
What makes this different
Taught by working data engineers
Your mentors build data platforms for a living. They bring real incidents, real architecture decisions, and real interview standards into the classroom.
One job role, taught in full depth
We don't cover data science, web development, and cloud admin in one package. We teach data engineering, completely, because that's what gets you hired.
Built for zero technical background
We start from what a variable is. No assumed knowledge, no skipped fundamentals, no “you should already know this.”
What you'll learn, in order
Each stage depends on the last. This is a sequence, not a menu. no skipping ahead.
- 01
Foundations
Foundations
SQL and Python, taught in that order: query real data first, then build the pipeline that moves it.
Query and code with real business data
- 02
Pipelines & Data
Pipelines & Data
Live APIs, PostgreSQL, Pandas (correctly scoped), and a real data-quality gate.
Build a pipeline that survives bad data
- 03
Engineering Practice
Engineering Practice
Git, code review, and Docker: working like a real engineering team, not solo.
Ship reviewed, reproducible code
- 04
Cloud & Warehouse
Cloud & Warehouse
Azure and Microsoft Fabric, querying a Fabric Warehouse (plus Snowflake): the pipeline leaves your laptop for good.
Run pipelines in the cloud, queried in a warehouse
- 05
Orchestration & Ops
Orchestration & Ops
dbt, Fabric Data Factory (plus Airflow), testing, and CI/CD: scheduled, tested, and monitored like production.
Operate a pipeline, not just write one
- 06
Scale
Scale
Spark for data too big for one machine, Fabric Eventstream (plus Kafka) for data that never stops arriving.
Handle real production scale
What you'll have built by the end.
Every learner ships an end-to-end pipeline they can defend in an interview. Not a tutorial followed to completion, but a system with decisions in it they can explain.
A learner's capstone goes on this page when they have agreed to it. Not before.
We could fill this grid with screenshots today. They would be ours, not a learner's, and you would have no way of telling the difference. Instead, here is the shape every capstone has to build, which is the part that actually tells you what the work is.
- sourcefiles, apps, DBs
- extractpython
- transformdbt · spark
- warehousesnowflake
- decisionthe number
Learner outcomes
The part of a bootcamp website you should trust least, and the part we are slowest to fill.
We have not published a learner outcome yet, because we do not have one we can put a real name to.
Quotes are the easiest thing on a website to invent and the hardest thing to check. This page stays empty until someone finishes the programme, lands a role, and agrees to say so under their own name. If that is slower than the competition, it is slower for a reason you can verify.
Frequently asked questions
Your first data engineering job starts here.
No sales pressure. We'll tell you if this isn't right for you.